research papers on the role of education

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 12 February 2024

Education reform and change driven by digital technology: a bibliometric study from a global perspective

• Chengliang Wang 1 ,
• Xiaojiao Chen 1 ,
• Teng Yu   ORCID: orcid.org/0000-0001-5198-7261 2 , 3 ,
• Yidan Liu 1 , 4 &
• Yuhui Jing 1  

Humanities and Social Sciences Communications volume  11 , Article number:  256 ( 2024 ) Cite this article

5247 Accesses

2 Citations

1 Altmetric

Metrics details

• Development studies
• Science, technology and society

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

Similar content being viewed by others

A bibliometric analysis of knowledge mapping in Chinese education digitalization research from 2012 to 2022

Digital transformation and digital literacy in the context of complexity within higher education institutions: a systematic literature review

Education big data and learning analytics: a bibliometric analysis

Introduction.

Digital technology has become an essential component of modern education, facilitating the extension of temporal and spatial boundaries and enriching the pedagogical contexts (Selwyn and Facer, 2014 ). The advent of mobile communication technology has enabled learning through social media platforms (Szeto et al. 2015 ; Pires et al. 2022 ), while the advancement of augmented reality technology has disrupted traditional conceptions of learning environments and spaces (Perez-Sanagustin et al., 2014 ; Kyza and Georgiou, 2018 ). A wide range of digital technologies has enabled learning to become a norm in various settings, including the workplace (Sjöberg and Holmgren, 2021 ), home (Nazare et al. 2022 ), and online communities (Tang and Lam, 2014 ). Education is no longer limited to fixed locations and schedules, but has permeated all aspects of life, allowing learning to continue at any time and any place (Camilleri and Camilleri, 2016 ; Selwyn and Facer, 2014 ).

The advent of digital technology has led to the creation of several informal learning environments (Greenhow and Lewin, 2015 ) that exhibit divergent form, function, features, and patterns in comparison to conventional learning environments (Nygren et al. 2019 ). Consequently, the associated teaching and learning processes, as well as the strategies for the creation, dissemination, and acquisition of learning resources, have undergone a complete overhaul. The ensuing transformations have posed a myriad of novel issues, such as the optimal structuring of teaching methods by instructors and the adoption of appropriate learning strategies by students in the new digital technology environment. Consequently, an examination of the principles that underpin effective teaching and learning in this environment is a topic of significant interest to numerous scholars engaged in digital technology education research.

Over the course of the last two decades, digital technology has made significant strides in the field of education, notably in extending education time and space and creating novel educational contexts with sustainability. Despite research attempts to consolidate the application of digital technology in education, previous studies have only focused on specific aspects of digital technology, such as Pinto and Leite’s ( 2020 ) investigation into digital technology in higher education and Mustapha et al.’s ( 2021 ) examination of the role and value of digital technology in education during the pandemic. While these studies have provided valuable insights into the practical applications of digital technology in particular educational domains, they have not comprehensively explored the macro-mechanisms and internal logic of digital technology implementation in education. Additionally, these studies were conducted over a relatively brief period, making it challenging to gain a comprehensive understanding of the macro-dynamics and evolutionary process of digital technology in education. Some studies have provided an overview of digital education from an educational perspective but lack a precise understanding of technological advancement and change (Yang et al. 2022 ). Therefore, this study seeks to employ a systematic scientific approach to collate relevant research from 2000 to 2022, comprehend the internal logic and development trends of digital technology in education, and grasp the outstanding contribution of digital technology in promoting the sustainability of education in time and space. In summary, this study aims to address the following questions:

RQ1: Since the turn of the century, what is the productivity distribution of the field of digital technology education application research in terms of authorship, country/region, institutional and journal level?

RQ2: What is the development trend of research on the application of digital technology in education in the past two decades?

RQ3: What are the current frontiers of research on the application of digital technology in education?

Literature review

Although the term “digital technology” has become ubiquitous, a unified definition has yet to be agreed upon by scholars. Because the meaning of the word digital technology is closely related to the specific context. Within the educational research domain, Selwyn’s ( 2016 ) definition is widely favored by scholars (Pinto and Leite, 2020 ). Selwyn ( 2016 ) provides a comprehensive view of various concrete digital technologies and their applications in education through ten specific cases, such as immediate feedback in classes, orchestrating teaching, and community learning. Through these specific application scenarios, Selwyn ( 2016 ) argues that digital technology encompasses technologies associated with digital devices, including but not limited to tablets, smartphones, computers, and social media platforms (such as Facebook and YouTube). Furthermore, Further, the behavior of accessing the internet at any location through portable devices can be taken as an extension of the behavior of applying digital technology.

The evolving nature of digital technology has significant implications in the field of education. In the 1890s, the focus of digital technology in education was on comprehending the nuances of digital space, digital culture, and educational methodologies, with its connotations aligned more towards the idea of e-learning. The advent and subsequent widespread usage of mobile devices since the dawn of the new millennium have been instrumental in the rapid expansion of the concept of digital technology. Notably, mobile learning devices such as smartphones and tablets, along with social media platforms, have become integral components of digital technology (Conole and Alevizou, 2010 ; Batista et al. 2016 ). In recent times, the burgeoning application of AI technology in the education sector has played a vital role in enriching the digital technology lexicon (Banerjee et al. 2021 ). ChatGPT, for instance, is identified as a novel educational technology that has immense potential to revolutionize future education (Rospigliosi, 2023 ; Arif, Munaf and Ul-Haque, 2023 ).

Pinto and Leite ( 2020 ) conducted a comprehensive macroscopic survey of the use of digital technologies in the education sector and identified three distinct categories, namely technologies for assessment and feedback, mobile technologies, and Information Communication Technologies (ICT). This classification criterion is both macroscopic and highly condensed. In light of the established concept definitions of digital technology in the educational research literature, this study has adopted the characterizations of digital technology proposed by Selwyn ( 2016 ) and Pinto and Leite ( 2020 ) as crucial criteria for analysis and research inclusion. Specifically, this criterion encompasses several distinct types of digital technologies, including Information and Communication Technologies (ICT), Mobile tools, eXtended Reality (XR) Technologies, Assessment and Feedback systems, Learning Management Systems (LMS), Publish and Share tools, Collaborative systems, Social media, Interpersonal Communication tools, and Content Aggregation tools.

Methodology and materials

Research method: bibliometric.

The research on econometric properties has been present in various aspects of human production and life, yet systematic scientific theoretical guidance has been lacking, resulting in disorganization. In 1969, British scholar Pritchard ( 1969 ) proposed “bibliometrics,” which subsequently emerged as an independent discipline in scientific quantification research. Initially, Pritchard defined bibliometrics as “the application of mathematical and statistical methods to books and other media of communication,” however, the definition was not entirely rigorous. To remedy this, Hawkins ( 2001 ) expanded Pritchard’s definition to “the quantitative analysis of the bibliographic features of a body of literature.” De Bellis further clarified the objectives of bibliometrics, stating that it aims to analyze and identify patterns in literature, such as the most productive authors, institutions, countries, and journals in scientific disciplines, trends in literary production over time, and collaboration networks (De Bellis, 2009 ). According to Garfield ( 2006 ), bibliometric research enables the examination of the history and structure of a field, the flow of information within the field, the impact of journals, and the citation status of publications over a longer time scale. All of these definitions illustrate the unique role of bibliometrics as a research method for evaluating specific research fields.

This study uses CiteSpace, VOSviewer, and Charticulator to analyze data and create visualizations. Each of these three tools has its own strengths and can complement each other. CiteSpace and VOSviewer use set theory and probability theory to provide various visualization views in fields such as keywords, co-occurrence, and co-authors. They are easy to use and produce visually appealing graphics (Chen, 2006 ; van Eck and Waltman, 2009 ) and are currently the two most widely used bibliometric tools in the field of visualization (Pan et al. 2018 ). In this study, VOSviewer provided the data necessary for the Performance Analysis; Charticulator was then used to redraw using the tabular data exported from VOSviewer (for creating the chord diagram of country collaboration); this was to complement the mapping process, while CiteSpace was primarily utilized to generate keyword maps and conduct burst word analysis.

Data retrieval

This study selected documents from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) in the Web of Science Core Collection as the data source, for the following reasons:

(1) The Web of Science Core Collection, as a high-quality digital literature resource database, has been widely accepted by many researchers and is currently considered the most suitable database for bibliometric analysis (Jing et al. 2023a ). Compared to other databases, Web of Science provides more comprehensive data information (Chen et al. 2022a ), and also provides data formats suitable for analysis using VOSviewer and CiteSpace (Gaviria-Marin et al. 2019 ).

(2) The application of digital technology in the field of education is an interdisciplinary research topic, involving technical knowledge literature belonging to the natural sciences and education-related literature belonging to the social sciences. Therefore, it is necessary to select Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) as the sources of research data, ensuring the comprehensiveness of data while ensuring the reliability and persuasiveness of bibliometric research (Hwang and Tsai, 2011 ; Wang et al. 2022 ).

After establishing the source of research data, it is necessary to determine a retrieval strategy (Jing et al. 2023b ). The choice of a retrieval strategy should consider a balance between the breadth and precision of the search formula. That is to say, it should encompass all the literature pertaining to the research topic while excluding irrelevant documents as much as possible. In light of this, this study has set a retrieval strategy informed by multiple related papers (Mustapha et al. 2021 ; Luo et al. 2021 ). The research by Mustapha et al. ( 2021 ) guided us in selecting keywords (“digital” AND “technolog*”) to target digital technology, while Luo et al. ( 2021 ) informed the selection of terms (such as “instruct*,” “teach*,” and “education”) to establish links with the field of education. Then, based on the current application of digital technology in the educational domain and the scope of selection criteria, we constructed the final retrieval strategy. Following the general patterns of past research (Jing et al. 2023a , 2023b ), we conducted a specific screening using the topic search (Topics, TS) function in Web of Science. For the specific criteria used in the screening for this study, please refer to Table 1 .

Literature screening

Literature acquired through keyword searches may contain ostensibly related yet actually unrelated works. Therefore, to ensure the close relevance of literature included in the analysis to the research topic, it is often necessary to perform a manual screening process to identify the final literature to be analyzed, subsequent to completing the initial literature search.

The manual screening process consists of two steps. Initially, irrelevant literature is weeded out based on the title and abstract, with two members of the research team involved in this phase. This stage lasted about one week, resulting in 1106 articles being retained. Subsequently, a comprehensive review of the full text is conducted to accurately identify the literature required for the study. To carry out the second phase of manual screening effectively and scientifically, and to minimize the potential for researcher bias, the research team established the inclusion criteria presented in Table 2 . Three members were engaged in this phase, which took approximately 2 weeks, culminating in the retention of 588 articles after meticulous screening. The entire screening process is depicted in Fig. 1 , adhering to the PRISMA guidelines (Page et al. 2021 ).

The process of obtaining and filtering the necessary literature data for research.

Data standardization

Nguyen and Hallinger ( 2020 ) pointed out that raw data extracted from scientific databases often contains multiple expressions of the same term, and not addressing these synonymous expressions could affect research results in bibliometric analysis. For instance, in the original data, the author list may include “Tsai, C. C.” and “Tsai, C.-C.”, while the keyword list may include “professional-development” and “professional development,” which often require merging. Therefore, before analyzing the selected literature, a data disambiguation process is necessary to standardize the data (Strotmann and Zhao, 2012 ; Van Eck and Waltman, 2019 ). This study adopted the data standardization process proposed by Taskin and Al ( 2019 ), mainly including the following standardization operations:

Firstly, the author and source fields in the data are corrected and standardized to differentiate authors with similar names.

Secondly, the study checks whether the journals to which the literature belongs have been renamed in the past over 20 years, so as to avoid the influence of periodical name change on the analysis results.

Finally, the keyword field is standardized by unifying parts of speech and singular/plural forms of keywords, which can help eliminate redundant entries in the knowledge graph.

Performance analysis (RQ1)

This section offers a thorough and detailed analysis of the state of research in the field of digital technology education. By utilizing descriptive statistics and visual maps, it provides a comprehensive overview of the development trends, authors, countries, institutions, and journal distribution within the field. The insights presented in this section are of great significance in advancing our understanding of the current state of research in this field and identifying areas for further investigation. The use of visual aids to display inter-country cooperation and the evolution of the field adds to the clarity and coherence of the analysis.

Time trend of the publications

To understand a research field, it is first necessary to understand the most basic quantitative information, among which the change in the number of publications per year best reflects the development trend of a research field. Figure 2 shows the distribution of publication dates.

Time trend of the publications on application of digital technology in education.

From the Fig. 2 , it can be seen that the development of this field over the past over 20 years can be roughly divided into three stages. The first stage was from 2000 to 2007, during which the number of publications was relatively low. Due to various factors such as technological maturity, the academic community did not pay widespread attention to the role of digital technology in expanding the scope of teaching and learning. The second stage was from 2008 to 2019, during which the overall number of publications showed an upward trend, and the development of the field entered an accelerated period, attracting more and more scholars’ attention. The third stage was from 2020 to 2022, during which the number of publications stabilized at around 100. During this period, the impact of the pandemic led to a large number of scholars focusing on the role of digital technology in education during the pandemic, and research on the application of digital technology in education became a core topic in social science research.

Analysis of authors

An analysis of the author’s publication volume provides information about the representative scholars and core research strengths of a research area. Table 3 presents information on the core authors in adaptive learning research, including name, publication number, and average number of citations per article (based on the analysis and statistics from VOSviewer).

Variations in research foci among scholars abound. Within the field of digital technology education application research over the past two decades, Neil Selwyn stands as the most productive author, having published 15 papers garnering a total of 1027 citations, resulting in an average of 68.47 citations per paper. As a Professor at the Faculty of Education at Monash University, Selwyn concentrates on exploring the application of digital technology in higher education contexts (Selwyn et al. 2021 ), as well as related products in higher education such as Coursera, edX, and Udacity MOOC platforms (Bulfin et al. 2014 ). Selwyn’s contributions to the educational sociology perspective include extensive research on the impact of digital technology on education, highlighting the spatiotemporal extension of educational processes and practices through technological means as the greatest value of educational technology (Selwyn, 2012 ; Selwyn and Facer, 2014 ). In addition, he provides a blueprint for the development of future schools in 2030 based on the present impact of digital technology on education (Selwyn et al. 2019 ). The second most productive author in this field, Henderson, also offers significant contributions to the understanding of the important value of digital technology in education, specifically in the higher education setting, with a focus on the impact of the pandemic (Henderson et al. 2015 ; Cohen et al. 2022 ). In contrast, Edwards’ research interests focus on early childhood education, particularly the application of digital technology in this context (Edwards, 2013 ; Bird and Edwards, 2015 ). Additionally, on the technical level, Edwards also mainly prefers digital game technology, because it is a digital technology that children are relatively easy to accept (Edwards, 2015 ).

Analysis of countries/regions and organization

The present study aimed to ascertain the leading countries in digital technology education application research by analyzing 75 countries related to 558 works of literature. Table 4 depicts the top ten countries that have contributed significantly to this field in terms of publication count (based on the analysis and statistics from VOSviewer). Our analysis of Table 4 data shows that England emerged as the most influential country/region, with 92 published papers and 2401 citations. Australia and the United States secured the second and third ranks, respectively, with 90 papers (2187 citations) and 70 papers (1331 citations) published. Geographically, most of the countries featured in the top ten publication volumes are situated in Australia, North America, and Europe, with China being the only exception. Notably, all these countries, except China, belong to the group of developed nations, suggesting that economic strength is a prerequisite for fostering research in the digital technology education application field.

This study presents a visual representation of the publication output and cooperation relationships among different countries in the field of digital technology education application research. Specifically, a chord diagram is employed to display the top 30 countries in terms of publication output, as depicted in Fig. 3 . The chord diagram is composed of nodes and chords, where the nodes are positioned as scattered points along the circumference, and the length of each node corresponds to the publication output, with longer lengths indicating higher publication output. The chords, on the other hand, represent the cooperation relationships between any two countries, and are weighted based on the degree of closeness of the cooperation, with wider chords indicating closer cooperation. Through the analysis of the cooperation relationships, the findings suggest that the main publishing countries in this field are engaged in cooperative relationships with each other, indicating a relatively high level of international academic exchange and research internationalization.

In the diagram, nodes are scattered along the circumference of a circle, with the length of each node representing the volume of publications. The weighted arcs connecting any two points on the circle are known as chords, representing the collaborative relationship between the two, with the width of the arc indicating the closeness of the collaboration.

Further analyzing Fig. 3 , we can extract more valuable information, enabling a deeper understanding of the connections between countries in the research field of digital technology in educational applications. It is evident that certain countries, such as the United States, China, and England, display thicker connections, indicating robust collaborative relationships in terms of productivity. These thicker lines signify substantial mutual contributions and shared objectives in certain sectors or fields, highlighting the interconnectedness and global integration in these areas. By delving deeper, we can also explore potential future collaboration opportunities through the chord diagram, identifying possible partners to propel research and development in this field. In essence, the chord diagram successfully encapsulates and conveys the multi-dimensionality of global productivity and cooperation, allowing for a comprehensive understanding of the intricate inter-country relationships and networks in a global context, providing valuable guidance and insights for future research and collaborations.

An in-depth examination of the publishing institutions is provided in Table 5 , showcasing the foremost 10 institutions ranked by their publication volume. Notably, Monash University and Australian Catholic University, situated in Australia, have recorded the most prolific publications within the digital technology education application realm, with 22 and 10 publications respectively. Moreover, the University of Oslo from Norway is featured among the top 10 publishing institutions, with an impressive average citation count of 64 per publication. It is worth highlighting that six institutions based in the United Kingdom were also ranked within the top 10 publishing institutions, signifying their leading position in this area of research.

Analysis of journals

Journals are the main carriers for publishing high-quality papers. Some scholars point out that the two key factors to measure the influence of journals in the specified field are the number of articles published and the number of citations. The more papers published in a magazine and the more citations, the greater its influence (Dzikowski, 2018 ). Therefore, this study utilized VOSviewer to statistically analyze the top 10 journals with the most publications in the field of digital technology in education and calculated the average citations per article (see Table 6 ).

Based on Table 6 , it is apparent that the highest number of articles in the domain of digital technology in education research were published in Education and Information Technologies (47 articles), Computers & Education (34 articles), and British Journal of Educational Technology (32 articles), indicating a higher article output compared to other journals. This underscores the fact that these three journals concentrate more on the application of digital technology in education. Furthermore, several other journals, such as Technology Pedagogy and Education and Sustainability, have published more than 15 articles in this domain. Sustainability represents the open access movement, which has notably facilitated research progress in this field, indicating that the development of open access journals in recent years has had a significant impact. Although there is still considerable disagreement among scholars on the optimal approach to achieve open access, the notion that research outcomes should be accessible to all is widely recognized (Huang et al. 2020 ). On further analysis of the research fields to which these journals belong, except for Sustainability, it is evident that they all pertain to educational technology, thus providing a qualitative definition of the research area of digital technology education from the perspective of journals.

Temporal keyword analysis: thematic evolution (RQ2)

The evolution of research themes is a dynamic process, and previous studies have attempted to present the developmental trajectory of fields by drawing keyword networks in phases (Kumar et al. 2021 ; Chen et al. 2022b ). To understand the shifts in research topics across different periods, this study follows past research and, based on the significant changes in the research field and corresponding technological advancements during the outlined periods, divides the timeline into four stages (the first stage from January 2000 to December 2005, the second stage from January 2006 to December 2011, the third stage from January 2012 to December 2017; and the fourth stage from January 2018 to December 2022). The division into these four stages was determined through a combination of bibliometric analysis and literature review, which presented a clear trajectory of the field’s development. The research analyzes the keyword networks for each time period (as there are only three articles in the first stage, it was not possible to generate an appropriate keyword co-occurrence map, hence only the keyword co-occurrence maps from the second to the fourth stages are provided), to understand the evolutionary track of the digital technology education application research field over time.

2000.1–2005.12: germination period

From January 2000 to December 2005, digital technology education application research was in its infancy. Only three studies focused on digital technology, all of which were related to computers. Due to the popularity of computers, the home became a new learning environment, highlighting the important role of digital technology in expanding the scope of learning spaces (Sutherland et al. 2000 ). In specific disciplines and contexts, digital technology was first favored in medical clinical practice, becoming an important tool for supporting the learning of clinical knowledge and practice (Tegtmeyer et al. 2001 ; Durfee et al. 2003 ).

2006.1–2011.12: initial development period

Between January 2006 and December 2011, it was the initial development period of digital technology education research. Significant growth was observed in research related to digital technology, and discussions and theoretical analyses about “digital natives” emerged. During this phase, scholars focused on the debate about “how to use digital technology reasonably” and “whether current educational models and school curriculum design need to be adjusted on a large scale” (Bennett and Maton, 2010 ; Selwyn, 2009 ; Margaryan et al. 2011 ). These theoretical and speculative arguments provided a unique perspective on the impact of cognitive digital technology on education and teaching. As can be seen from the vocabulary such as “rethinking”, “disruptive pedagogy”, and “attitude” in Fig. 4 , many scholars joined the calm reflection and analysis under the trend of digital technology (Laurillard, 2008 ; Vratulis et al. 2011 ). During this phase, technology was still undergoing dramatic changes. The development of mobile technology had already caught the attention of many scholars (Wong et al. 2011 ), but digital technology represented by computers was still very active (Selwyn et al. 2011 ). The change in technological form would inevitably lead to educational transformation. Collins and Halverson ( 2010 ) summarized the prospects and challenges of using digital technology for learning and educational practices, believing that digital technology would bring a disruptive revolution to the education field and bring about a new educational system. In addition, the term “teacher education” in Fig. 4 reflects the impact of digital technology development on teachers. The rapid development of technology has widened the generation gap between teachers and students. To ensure smooth communication between teachers and students, teachers must keep up with the trend of technological development and establish a lifelong learning concept (Donnison, 2009 ).

In the diagram, each node represents a keyword, with the size of the node indicating the frequency of occurrence of the keyword. The connections represent the co-occurrence relationships between keywords, with a higher frequency of co-occurrence resulting in tighter connections.

2012.1–2017.12: critical exploration period

During the period spanning January 2012 to December 2017, the application of digital technology in education research underwent a significant exploration phase. As can be seen from Fig. 5 , different from the previous stage, the specific elements of specific digital technology have started to increase significantly, including the enrichment of technological contexts, the greater variety of research methods, and the diversification of learning modes. Moreover, the temporal and spatial dimensions of the learning environment were further de-emphasized, as noted in previous literature (Za et al. 2014 ). Given the rapidly accelerating pace of technological development, the education system in the digital era is in urgent need of collaborative evolution and reconstruction, as argued by Davis, Eickelmann, and Zaka ( 2013 ).

In the domain of digital technology, social media has garnered substantial scholarly attention as a promising avenue for learning, as noted by Pasquini and Evangelopoulos ( 2016 ). The implementation of social media in education presents several benefits, including the liberation of education from the restrictions of physical distance and time, as well as the erasure of conventional educational boundaries. The user-generated content (UGC) model in social media has emerged as a crucial source for knowledge creation and distribution, with the widespread adoption of mobile devices. Moreover, social networks have become an integral component of ubiquitous learning environments (Hwang et al. 2013 ). The utilization of social media allows individuals to function as both knowledge producers and recipients, which leads to a blurring of the conventional roles of learners and teachers. On mobile platforms, the roles of learners and teachers are not fixed, but instead interchangeable.

In terms of research methodology, the prevalence of empirical studies with survey designs in the field of educational technology during this period is evident from the vocabulary used, such as “achievement,” “acceptance,” “attitude,” and “ict.” in Fig. 5 . These studies aim to understand learners’ willingness to adopt and attitudes towards new technologies, and some seek to investigate the impact of digital technologies on learning outcomes through quasi-experimental designs (Domínguez et al. 2013 ). Among these empirical studies, mobile learning emerged as a hot topic, and this is not surprising. First, the advantages of mobile learning environments over traditional ones have been empirically demonstrated (Hwang et al. 2013 ). Second, learners born around the turn of the century have been heavily influenced by digital technologies and have developed their own learning styles that are more open to mobile devices as a means of learning. Consequently, analyzing mobile learning as a relatively novel mode of learning has become an important issue for scholars in the field of educational technology.

The intervention of technology has led to the emergence of several novel learning modes, with the blended learning model being the most representative one in the current phase. Blended learning, a novel concept introduced in the information age, emphasizes the integration of the benefits of traditional learning methods and online learning. This learning mode not only highlights the prominent role of teachers in guiding, inspiring, and monitoring the learning process but also underlines the importance of learners’ initiative, enthusiasm, and creativity in the learning process. Despite being an early conceptualization, blended learning’s meaning has been expanded by the widespread use of mobile technology and social media in education. The implementation of new technologies, particularly mobile devices, has resulted in the transformation of curriculum design and increased flexibility and autonomy in students’ learning processes (Trujillo Maza et al. 2016 ), rekindling scholarly attention to this learning mode. However, some scholars have raised concerns about the potential drawbacks of the blended learning model, such as its significant impact on the traditional teaching system, the lack of systematic coping strategies and relevant policies in several schools and regions (Moskal et al. 2013 ).

2018.1–2022.12: accelerated transformation period

The period spanning from January 2018 to December 2022 witnessed a rapid transformation in the application of digital technology in education research. The field of digital technology education research reached a peak period of publication, largely influenced by factors such as the COVID-19 pandemic (Yu et al. 2023 ). Research during this period was built upon the achievements, attitudes, and social media of the previous phase, and included more elements that reflect the characteristics of this research field, such as digital literacy, digital competence, and professional development, as depicted in Fig. 6 . Alongside this, scholars’ expectations for the value of digital technology have expanded, and the pursuit of improving learning efficiency and performance is no longer the sole focus. Some research now aims to cultivate learners’ motivation and enhance their self-efficacy by applying digital technology in a reasonable manner, as demonstrated by recent studies (Beardsley et al. 2021 ; Creely et al. 2021 ).

The COVID-19 pandemic has emerged as a crucial backdrop for the digital technology’s role in sustaining global education, as highlighted by recent scholarly research (Zhou et al. 2022 ; Pan and Zhang, 2020 ; Mo et al. 2022 ). The online learning environment, which is supported by digital technology, has become the primary battleground for global education (Yu, 2022 ). This social context has led to various studies being conducted, with some scholars positing that the pandemic has impacted the traditional teaching order while also expanding learning possibilities in terms of patterns and forms (Alabdulaziz, 2021 ). Furthermore, the pandemic has acted as a catalyst for teacher teaching and technological innovation, and this viewpoint has been empirically substantiated (Moorhouse and Wong, 2021 ). Additionally, some scholars believe that the pandemic’s push is a crucial driving force for the digital transformation of the education system, serving as an essential mechanism for overcoming the system’s inertia (Romero et al. 2021 ).

The rapid outbreak of the pandemic posed a challenge to the large-scale implementation of digital technologies, which was influenced by a complex interplay of subjective and objective factors. Objective constraints included the lack of infrastructure in some regions to support digital technologies, while subjective obstacles included psychological resistance among certain students and teachers (Moorhouse, 2021 ). These factors greatly impacted the progress of online learning during the pandemic. Additionally, Timotheou et al. ( 2023 ) conducted a comprehensive systematic review of existing research on digital technology use during the pandemic, highlighting the critical role played by various factors such as learners’ and teachers’ digital skills, teachers’ personal attributes and professional development, school leadership and management, and administration in facilitating the digitalization and transformation of schools.

The current stage of research is characterized by the pivotal term “digital literacy,” denoting a growing interest in learners’ attitudes and adoption of emerging technologies. Initially, the term “literacy” was restricted to fundamental abilities and knowledge associated with books and print materials (McMillan, 1996 ). However, with the swift advancement of computers and digital technology, there have been various attempts to broaden the scope of literacy beyond its traditional meaning, including game literacy (Buckingham and Burn, 2007 ), information literacy (Eisenberg, 2008 ), and media literacy (Turin and Friesem, 2020 ). Similarly, digital literacy has emerged as a crucial concept, and Gilster and Glister ( 1997 ) were the first to introduce this concept, referring to the proficiency in utilizing technology and processing digital information in academic, professional, and daily life settings. In practical educational settings, learners who possess higher digital literacy often exhibit an aptitude for quickly mastering digital devices and applying them intelligently to education and teaching (Yu, 2022 ).

The utilization of digital technology in education has undergone significant changes over the past two decades, and has been a crucial driver of educational reform with each new technological revolution. The impact of these changes on the underlying logic of digital technology education applications has been noticeable. From computer technology to more recent developments such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), the acceleration in digital technology development has been ongoing. Educational reforms spurred by digital technology development continue to be dynamic, as each new digital innovation presents new possibilities and models for teaching practice. This is especially relevant in the post-pandemic era, where the importance of technological progress in supporting teaching cannot be overstated (Mughal et al. 2022 ). Existing digital technologies have already greatly expanded the dimensions of education in both time and space, while future digital technologies aim to expand learners’ perceptions. Researchers have highlighted the potential of integrated technology and immersive technology in the development of the educational metaverse, which is highly anticipated to create a new dimension for the teaching and learning environment, foster a new value system for the discipline of educational technology, and more effectively and efficiently achieve the grand educational blueprint of the United Nations’ Sustainable Development Goals (Zhang et al. 2022 ; Li and Yu, 2023 ).

Hotspot evolution analysis (RQ3)

The examination of keyword evolution reveals a consistent trend in the advancement of digital technology education application research. The emergence and transformation of keywords serve as indicators of the varying research interests in this field. Thus, the utilization of the burst detection function available in CiteSpace allowed for the identification of the top 10 burst words that exhibited a high level of burst strength. This outcome is illustrated in Table 7 .

According to the results presented in Table 7 , the explosive terminology within the realm of digital technology education research has exhibited a concentration mainly between the years 2018 and 2022. Prior to this time frame, the emerging keywords were limited to “information technology” and “computer”. Notably, among them, computer, as an emergent keyword, has always had a high explosive intensity from 2008 to 2018, which reflects the important position of computer in digital technology and is the main carrier of many digital technologies such as Learning Management Systems (LMS) and Assessment and Feedback systems (Barlovits et al. 2022 ).

Since 2018, an increasing number of research studies have focused on evaluating the capabilities of learners to accept, apply, and comprehend digital technologies. As indicated by the use of terms such as “digital literacy” and “digital skill,” the assessment of learners’ digital literacy has become a critical task. Scholarly efforts have been directed towards the development of literacy assessment tools and the implementation of empirical assessments. Furthermore, enhancing the digital literacy of both learners and educators has garnered significant attention. (Nagle, 2018 ; Yu, 2022 ). Simultaneously, given the widespread use of various digital technologies in different formal and informal learning settings, promoting learners’ digital skills has become a crucial objective for contemporary schools (Nygren et al. 2019 ; Forde and OBrien, 2022 ).

Since 2020, the field of applied research on digital technology education has witnessed the emergence of three new hotspots, all of which have been affected to some extent by the pandemic. Firstly, digital technology has been widely applied in physical education, which is one of the subjects that has been severely affected by the pandemic (Parris et al. 2022 ; Jiang and Ning, 2022 ). Secondly, digital transformation has become an important measure for most schools, especially higher education institutions, to cope with the impact of the pandemic globally (García-Morales et al. 2021 ). Although the concept of digital transformation was proposed earlier, the COVID-19 pandemic has greatly accelerated this transformation process. Educational institutions must carefully redesign their educational products to face this new situation, providing timely digital learning methods, environments, tools, and support systems that have far-reaching impacts on modern society (Krishnamurthy, 2020 ; Salas-Pilco et al. 2022 ). Moreover, the professional development of teachers has become a key mission of educational institutions in the post-pandemic era. Teachers need to have a certain level of digital literacy and be familiar with the tools and online teaching resources used in online teaching, which has become a research hotspot today. Organizing digital skills training for teachers to cope with the application of emerging technologies in education is an important issue for teacher professional development and lifelong learning (Garzón-Artacho et al. 2021 ). As the main organizers and practitioners of emergency remote teaching (ERT) during the pandemic, teachers must put cognitive effort into their professional development to ensure effective implementation of ERT (Romero-Hall and Jaramillo Cherrez, 2022 ).

The burst word “digital transformation” reveals that we are in the midst of an ongoing digital technology revolution. With the emergence of innovative digital technologies such as ChatGPT and Microsoft 365 Copilot, technology trends will continue to evolve, albeit unpredictably. While the impact of these advancements on school education remains uncertain, it is anticipated that the widespread integration of technology will significantly affect the current education system. Rejecting emerging technologies without careful consideration is unwise. Like any revolution, the technological revolution in the education field has both positive and negative aspects. Detractors argue that digital technology disrupts learning and memory (Baron, 2021 ) or causes learners to become addicted and distracted from learning (Selwyn and Aagaard, 2020 ). On the other hand, the prudent use of digital technology in education offers a glimpse of a golden age of open learning. Educational leaders and practitioners have the opportunity to leverage cutting-edge digital technologies to address current educational challenges and develop a rational path for the sustainable and healthy growth of education.

Discussion on performance analysis (RQ1)

The field of digital technology education application research has experienced substantial growth since the turn of the century, a phenomenon that is quantifiably apparent through an analysis of authorship, country/region contributions, and institutional engagement. This expansion reflects the increased integration of digital technologies in educational settings and the heightened scholarly interest in understanding and optimizing their use.

Discussion on authorship productivity in digital technology education research

The authorship distribution within digital technology education research is indicative of the field’s intellectual structure and depth. A primary figure in this domain is Neil Selwyn, whose substantial citation rate underscores the profound impact of his work. His focus on the implications of digital technology in higher education and educational sociology has proven to be seminal. Selwyn’s research trajectory, especially the exploration of spatiotemporal extensions of education through technology, provides valuable insights into the multifaceted role of digital tools in learning processes (Selwyn et al. 2019 ).

Other notable contributors, like Henderson and Edwards, present diversified research interests, such as the impact of digital technologies during the pandemic and their application in early childhood education, respectively. Their varied focuses highlight the breadth of digital technology education research, encompassing pedagogical innovation, technological adaptation, and policy development.

Discussion on country/region-level productivity and collaboration

At the country/region level, the United Kingdom, specifically England, emerges as a leading contributor with 92 published papers and a significant citation count. This is closely followed by Australia and the United States, indicating a strong English-speaking research axis. Such geographical concentration of scholarly output often correlates with investment in research and development, technological infrastructure, and the prevalence of higher education institutions engaging in cutting-edge research.

China’s notable inclusion as the only non-Western country among the top contributors to the field suggests a growing research capacity and interest in digital technology in education. However, the lower average citation per paper for China could reflect emerging engagement or different research focuses that may not yet have achieved the same international recognition as Western counterparts.

The chord diagram analysis furthers this understanding, revealing dense interconnections between countries like the United States, China, and England, which indicates robust collaborations. Such collaborations are fundamental in addressing global educational challenges and shaping international research agendas.

Discussion on institutional-level contributions to digital technology education

Institutional productivity in digital technology education research reveals a constellation of universities driving the field forward. Monash University and the Australian Catholic University have the highest publication output, signaling Australia’s significant role in advancing digital education research. The University of Oslo’s remarkable average citation count per publication indicates influential research contributions, potentially reflecting high-quality studies that resonate with the broader academic community.

The strong showing of UK institutions, including the University of London, The Open University, and the University of Cambridge, reinforces the UK’s prominence in this research field. Such institutions are often at the forefront of pedagogical innovation, benefiting from established research cultures and funding mechanisms that support sustained inquiry into digital education.

Discussion on journal publication analysis

An examination of journal outputs offers a lens into the communicative channels of the field’s knowledge base. Journals such as Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology not only serve as the primary disseminators of research findings but also as indicators of research quality and relevance. The impact factor (IF) serves as a proxy for the quality and influence of these journals within the academic community.

The high citation counts for articles published in Computers & Education suggest that research disseminated through this medium has a wide-reaching impact and is of particular interest to the field. This is further evidenced by its significant IF of 11.182, indicating that the journal is a pivotal platform for seminal work in the application of digital technology in education.

The authorship, regional, and institutional productivity in the field of digital technology education application research collectively narrate the evolution of this domain since the turn of the century. The prominence of certain authors and countries underscores the importance of socioeconomic factors and existing academic infrastructure in fostering research productivity. Meanwhile, the centrality of specific journals as outlets for high-impact research emphasizes the role of academic publishing in shaping the research landscape.

As the field continues to grow, future research may benefit from leveraging the collaborative networks that have been elucidated through this analysis, perhaps focusing on underrepresented regions to broaden the scope and diversity of research. Furthermore, the stabilization of publication numbers in recent years invites a deeper exploration into potential plateaus in research trends or saturation in certain sub-fields, signaling an opportunity for novel inquiries and methodological innovations.

Discussion on the evolutionary trends (RQ2)

The evolution of the research field concerning the application of digital technology in education over the past two decades is a story of convergence, diversification, and transformation, shaped by rapid technological advancements and shifting educational paradigms.

At the turn of the century, the inception of digital technology in education was largely exploratory, with a focus on how emerging computer technologies could be harnessed to enhance traditional learning environments. Research from this early period was primarily descriptive, reflecting on the potential and challenges of incorporating digital tools into the educational setting. This phase was critical in establishing the fundamental discourse that would guide subsequent research, as it set the stage for understanding the scope and impact of digital technology in learning spaces (Wang et al. 2023 ).

As the first decade progressed, the narrative expanded to encompass the pedagogical implications of digital technologies. This was a period of conceptual debates, where terms like “digital natives” and “disruptive pedagogy” entered the academic lexicon, underscoring the growing acknowledgment of digital technology as a transformative force within education (Bennett and Maton, 2010 ). During this time, the research began to reflect a more nuanced understanding of the integration of technology, considering not only its potential to change where and how learning occurred but also its implications for educational equity and access.

In the second decade, with the maturation of internet connectivity and mobile technology, the focus of research shifted from theoretical speculations to empirical investigations. The proliferation of digital devices and the ubiquity of social media influenced how learners interacted with information and each other, prompting a surge in studies that sought to measure the impact of these tools on learning outcomes. The digital divide and issues related to digital literacy became central concerns, as scholars explored the varying capacities of students and educators to engage with technology effectively.

Throughout this period, there was an increasing emphasis on the individualization of learning experiences, facilitated by adaptive technologies that could cater to the unique needs and pacing of learners (Jing et al. 2023a ). This individualization was coupled with a growing recognition of the importance of collaborative learning, both online and offline, and the role of digital tools in supporting these processes. Blended learning models, which combined face-to-face instruction with online resources, emerged as a significant trend, advocating for a balance between traditional pedagogies and innovative digital strategies.

The later years, particularly marked by the COVID-19 pandemic, accelerated the necessity for digital technology in education, transforming it from a supplementary tool to an essential platform for delivering education globally (Mo et al. 2022 ; Mustapha et al. 2021 ). This era brought about an unprecedented focus on online learning environments, distance education, and virtual classrooms. Research became more granular, examining not just the pedagogical effectiveness of digital tools, but also their role in maintaining continuity of education during crises, their impact on teacher and student well-being, and their implications for the future of educational policy and infrastructure.

Across these two decades, the research field has seen a shift from examining digital technology as an external addition to the educational process, to viewing it as an integral component of curriculum design, instructional strategies, and even assessment methods. The emergent themes have broadened from a narrow focus on specific tools or platforms to include wider considerations such as data privacy, ethical use of technology, and the environmental impact of digital tools.

Moreover, the field has moved from considering the application of digital technology in education as a primarily cognitive endeavor to recognizing its role in facilitating socio-emotional learning, digital citizenship, and global competencies. Researchers have increasingly turned their attention to the ways in which technology can support collaborative skills, cultural understanding, and ethical reasoning within diverse student populations.

In summary, the past over twenty years in the research field of digital technology applications in education have been characterized by a progression from foundational inquiries to complex analyses of digital integration. This evolution has mirrored the trajectory of technology itself, from a facilitative tool to a pervasive ecosystem defining contemporary educational experiences. As we look to the future, the field is poised to delve into the implications of emerging technologies like AI, AR, and VR, and their potential to redefine the educational landscape even further. This ongoing metamorphosis suggests that the application of digital technology in education will continue to be a rich area of inquiry, demanding continual adaptation and forward-thinking from educators and researchers alike.

Discussion on the study of research hotspots (RQ3)

The analysis of keyword evolution in digital technology education application research elucidates the current frontiers in the field, reflecting a trajectory that is in tandem with the rapidly advancing digital age. This landscape is sculpted by emergent technological innovations and shaped by the demands of an increasingly digital society.

Interdisciplinary integration and pedagogical transformation

One of the frontiers identified from recent keyword bursts includes the integration of digital technology into diverse educational contexts, particularly noted with the keyword “physical education.” The digitalization of disciplines traditionally characterized by physical presence illustrates the pervasive reach of technology and signifies a push towards interdisciplinary integration where technology is not only a facilitator but also a transformative agent. This integration challenges educators to reconceptualize curriculum delivery to accommodate digital tools that can enhance or simulate the physical aspects of learning.

Digital literacy and skills acquisition

Another pivotal frontier is the focus on “digital literacy” and “digital skill”, which has intensified in recent years. This suggests a shift from mere access to technology towards a comprehensive understanding and utilization of digital tools. In this realm, the emphasis is not only on the ability to use technology but also on critical thinking, problem-solving, and the ethical use of digital resources (Yu, 2022 ). The acquisition of digital literacy is no longer an additive skill but a fundamental aspect of modern education, essential for navigating and contributing to the digital world.

Educational digital transformation

The keyword “digital transformation” marks a significant research frontier, emphasizing the systemic changes that education institutions must undergo to align with the digital era (Romero et al. 2021 ). This transformation includes the redesigning of learning environments, pedagogical strategies, and assessment methods to harness digital technology’s full potential. Research in this area explores the complexity of institutional change, addressing the infrastructural, cultural, and policy adjustments needed for a seamless digital transition.

Engagement and participation

Further exploration into “engagement” and “participation” underscores the importance of student-centered learning environments that are mediated by technology. The current frontiers examine how digital platforms can foster collaboration, inclusivity, and active learning, potentially leading to more meaningful and personalized educational experiences. Here, the use of technology seeks to support the emotional and cognitive aspects of learning, moving beyond the transactional view of education to one that is relational and interactive.

Professional development and teacher readiness

As the field evolves, “professional development” emerges as a crucial area, particularly in light of the pandemic which necessitated emergency remote teaching. The need for teacher readiness in a digital age is a pressing frontier, with research focusing on the competencies required for educators to effectively integrate technology into their teaching practices. This includes familiarity with digital tools, pedagogical innovation, and an ongoing commitment to personal and professional growth in the digital domain.

Pandemic as a catalyst

The recent pandemic has acted as a catalyst for accelerated research and application in this field, particularly in the domains of “digital transformation,” “professional development,” and “physical education.” This period has been a litmus test for the resilience and adaptability of educational systems to continue their operations in an emergency. Research has thus been directed at understanding how digital technologies can support not only continuity but also enhance the quality and reach of education in such contexts.

Ethical and societal considerations

The frontier of digital technology in education is also expanding to consider broader ethical and societal implications. This includes issues of digital equity, data privacy, and the sociocultural impact of technology on learning communities. The research explores how educational technology can be leveraged to address inequities and create more equitable learning opportunities for all students, regardless of their socioeconomic background.

Innovation and emerging technologies

Looking forward, the frontiers are set to be influenced by ongoing and future technological innovations, such as artificial intelligence (AI) (Wu and Yu, 2023 ; Chen et al. 2022a ). The exploration into how these technologies can be integrated into educational practices to create immersive and adaptive learning experiences represents a bold new chapter for the field.

In conclusion, the current frontiers of research on the application of digital technology in education are multifaceted and dynamic. They reflect an overarching movement towards deeper integration of technology in educational systems and pedagogical practices, where the goals are not only to facilitate learning but to redefine it. As these frontiers continue to expand and evolve, they will shape the educational landscape, requiring a concerted effort from researchers, educators, policymakers, and technologists to navigate the challenges and harness the opportunities presented by the digital revolution in education.

Conclusions and future research

Conclusions.

The utilization of digital technology in education is a research area that cuts across multiple technical and educational domains and continues to experience dynamic growth due to the continuous progress of technology. In this study, a systematic review of this field was conducted through bibliometric techniques to examine its development trajectory. The primary focus of the review was to investigate the leading contributors, productive national institutions, significant publications, and evolving development patterns. The study’s quantitative analysis resulted in several key conclusions that shed light on this research field’s current state and future prospects.

(1) The research field of digital technology education applications has entered a stage of rapid development, particularly in recent years due to the impact of the pandemic, resulting in a peak of publications. Within this field, several key authors (Selwyn, Henderson, Edwards, etc.) and countries/regions (England, Australia, USA, etc.) have emerged, who have made significant contributions. International exchanges in this field have become frequent, with a high degree of internationalization in academic research. Higher education institutions in the UK and Australia are the core productive forces in this field at the institutional level.

(2) Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology are notable journals that publish research related to digital technology education applications. These journals are affiliated with the research field of educational technology and provide effective communication platforms for sharing digital technology education applications.

(3) Over the past two decades, research on digital technology education applications has progressed from its early stages of budding, initial development, and critical exploration to accelerated transformation, and it is currently approaching maturity. Technological progress and changes in the times have been key driving forces for educational transformation and innovation, and both have played important roles in promoting the continuous development of education.

(4) Influenced by the pandemic, three emerging frontiers have emerged in current research on digital technology education applications, which are physical education, digital transformation, and professional development under the promotion of digital technology. These frontier research hotspots reflect the core issues that the education system faces when encountering new technologies. The evolution of research hotspots shows that technology breakthroughs in education’s original boundaries of time and space create new challenges. The continuous self-renewal of education is achieved by solving one hotspot problem after another.

The present study offers significant practical implications for scholars and practitioners in the field of digital technology education applications. Firstly, it presents a well-defined framework of the existing research in this area, serving as a comprehensive guide for new entrants to the field and shedding light on the developmental trajectory of this research domain. Secondly, the study identifies several contemporary research hotspots, thus offering a valuable decision-making resource for scholars aiming to explore potential research directions. Thirdly, the study undertakes an exhaustive analysis of published literature to identify core journals in the field of digital technology education applications, with Sustainability being identified as a promising open access journal that publishes extensively on this topic. This finding can potentially facilitate scholars in selecting appropriate journals for their research outputs.

Limitation and future research

Influenced by some objective factors, this study also has some limitations. First of all, the bibliometrics analysis software has high standards for data. In order to ensure the quality and integrity of the collected data, the research only selects the periodical papers in SCIE and SSCI indexes, which are the core collection of Web of Science database, and excludes other databases, conference papers, editorials and other publications, which may ignore some scientific research and original opinions in the field of digital technology education and application research. In addition, although this study used professional software to carry out bibliometric analysis and obtained more objective quantitative data, the analysis and interpretation of data will inevitably have a certain subjective color, and the influence of subjectivity on data analysis cannot be completely avoided. As such, future research endeavors will broaden the scope of literature screening and proactively engage scholars in the field to gain objective and state-of-the-art insights, while minimizing the adverse impact of personal subjectivity on research analysis.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/F9QMHY

Alabdulaziz MS (2021) COVID-19 and the use of digital technology in mathematics education. Educ Inf Technol 26(6):7609–7633. https://doi.org/10.1007/s10639-021-10602-3

Arif TB, Munaf U, Ul-Haque I (2023) The future of medical education and research: is ChatGPT a blessing or blight in disguise? Med Educ Online 28. https://doi.org/10.1080/10872981.2023.2181052

Banerjee M, Chiew D, Patel KT, Johns I, Chappell D, Linton N, Cole GD, Francis DP, Szram J, Ross J, Zaman S (2021) The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers. BMC Med Educ 21. https://doi.org/10.1186/s12909-021-02870-x

Barlovits S, Caldeira A, Fesakis G, Jablonski S, Koutsomanoli Filippaki D, Lázaro C, Ludwig M, Mammana MF, Moura A, Oehler DXK, Recio T, Taranto E, Volika S(2022) Adaptive, synchronous, and mobile online education: developing the ASYMPTOTE learning environment. Mathematics 10:1628. https://doi.org/10.3390/math10101628

Article   Google Scholar  

Baron NS(2021) Know what? How digital technologies undermine learning and remembering J Pragmat 175:27–37. https://doi.org/10.1016/j.pragma.2021.01.011

Batista J, Morais NS, Ramos F (2016) Researching the use of communication technologies in higher education institutions in Portugal. https://doi.org/10.4018/978-1-5225-0571-6.ch057

Beardsley M, Albó L, Aragón P, Hernández-Leo D (2021) Emergency education effects on teacher abilities and motivation to use digital technologies. Br J Educ Technol 52. https://doi.org/10.1111/bjet.13101

Bennett S, Maton K(2010) Beyond the “digital natives” debate: towards a more nuanced understanding of students’ technology experiences J Comput Assist Learn 26:321–331. https://doi.org/10.1111/j.1365-2729.2010.00360.x

Buckingham D, Burn A (2007) Game literacy in theory and practice 16:323–349

Google Scholar  

Bulfin S, Pangrazio L, Selwyn N (2014) Making “MOOCs”: the construction of a new digital higher education within news media discourse. In: The International Review of Research in Open and Distributed Learning 15. https://doi.org/10.19173/irrodl.v15i5.1856

Camilleri MA, Camilleri AC(2016) Digital learning resources and ubiquitous technologies in education Technol Knowl Learn 22:65–82. https://doi.org/10.1007/s10758-016-9287-7

Chen C(2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature J Am Soc Inf Sci Technol 57:359–377. https://doi.org/10.1002/asi.20317

Chen J, Dai J, Zhu K, Xu L(2022) Effects of extended reality on language learning: a meta-analysis Front Psychol 13:1016519. https://doi.org/10.3389/fpsyg.2022.1016519

Article   PubMed   PubMed Central   Google Scholar  

Chen J, Wang CL, Tang Y (2022b) Knowledge mapping of volunteer motivation: a bibliometric analysis and cross-cultural comparative study. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.883150

Cohen A, Soffer T, Henderson M(2022) Students’ use of technology and their perceptions of its usefulness in higher education: International comparison J Comput Assist Learn 38(5):1321–1331. https://doi.org/10.1111/jcal.12678

Collins A, Halverson R(2010) The second educational revolution: rethinking education in the age of technology J Comput Assist Learn 26:18–27. https://doi.org/10.1111/j.1365-2729.2009.00339.x

Conole G, Alevizou P (2010) A literature review of the use of Web 2.0 tools in higher education. Walton Hall, Milton Keynes, UK: the Open University, retrieved 17 February

Creely E, Henriksen D, Crawford R, Henderson M(2021) Exploring creative risk-taking and productive failure in classroom practice. A case study of the perceived self-efficacy and agency of teachers at one school Think Ski Creat 42:100951. https://doi.org/10.1016/j.tsc.2021.100951

Davis N, Eickelmann B, Zaka P(2013) Restructuring of educational systems in the digital age from a co-evolutionary perspective J Comput Assist Learn 29:438–450. https://doi.org/10.1111/jcal.12032

De Belli N (2009) Bibliometrics and citation analysis: from the science citation index to cybermetrics, Scarecrow Press. https://doi.org/10.1111/jcal.12032

Domínguez A, Saenz-de-Navarrete J, de-Marcos L, Fernández-Sanz L, Pagés C, Martínez-Herráiz JJ(2013) Gamifying learning experiences: practical implications and outcomes Comput Educ 63:380–392. https://doi.org/10.1016/j.compedu.2012.12.020

Donnison S (2009) Discourses in conflict: the relationship between Gen Y pre-service teachers, digital technologies and lifelong learning. Australasian J Educ Technol 25. https://doi.org/10.14742/ajet.1138

Durfee SM, Jain S, Shaffer K (2003) Incorporating electronic media into medical student education. Acad Radiol 10:205–210. https://doi.org/10.1016/s1076-6332(03)80046-6

Dzikowski P(2018) A bibliometric analysis of born global firms J Bus Res 85:281–294. https://doi.org/10.1016/j.jbusres.2017.12.054

van Eck NJ, Waltman L(2009) Software survey: VOSviewer, a computer program for bibliometric mapping Scientometrics 84:523–538 https://doi.org/10.1007/s11192-009-0146-3

Edwards S(2013) Digital play in the early years: a contextual response to the problem of integrating technologies and play-based pedagogies in the early childhood curriculum Eur Early Child Educ Res J 21:199–212. https://doi.org/10.1080/1350293x.2013.789190

Edwards S(2015) New concepts of play and the problem of technology, digital media and popular-culture integration with play-based learning in early childhood education Technol Pedagogy Educ 25:513–532 https://doi.org/10.1080/1475939x.2015.1108929

Article   MathSciNet   Google Scholar  

Eisenberg MB(2008) Information literacy: essential skills for the information age DESIDOC J Libr Inf Technol 28:39–47. https://doi.org/10.14429/djlit.28.2.166

Forde C, OBrien A (2022) A literature review of barriers and opportunities presented by digitally enhanced practical skill teaching and learning in health science education. Med Educ Online 27. https://doi.org/10.1080/10872981.2022.2068210

García-Morales VJ, Garrido-Moreno A, Martín-Rojas R (2021) The transformation of higher education after the COVID disruption: emerging challenges in an online learning scenario. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.616059

Garfield E(2006) The history and meaning of the journal impact factor JAMA 295:90. https://doi.org/10.1001/jama.295.1.90

Article   PubMed   Google Scholar  

Garzón-Artacho E, Sola-Martínez T, Romero-Rodríguez JM, Gómez-García G(2021) Teachers’ perceptions of digital competence at the lifelong learning stage Heliyon 7:e07513. https://doi.org/10.1016/j.heliyon.2021.e07513

Gaviria-Marin M, Merigó JM, Baier-Fuentes H(2019) Knowledge management: a global examination based on bibliometric analysis Technol Forecast Soc Change 140:194–220. https://doi.org/10.1016/j.techfore.2018.07.006

Gilster P, Glister P (1997) Digital literacy. Wiley Computer Pub, New York

Greenhow C, Lewin C(2015) Social media and education: reconceptualizing the boundaries of formal and informal learning Learn Media Technol 41:6–30. https://doi.org/10.1080/17439884.2015.1064954

Hawkins DT(2001) Bibliometrics of electronic journals in information science Infor Res 7(1):7–1. http://informationr.net/ir/7-1/paper120.html

Henderson M, Selwyn N, Finger G, Aston R(2015) Students’ everyday engagement with digital technology in university: exploring patterns of use and “usefulness J High Educ Policy Manag 37:308–319 https://doi.org/10.1080/1360080x.2015.1034424

Huang CK, Neylon C, Hosking R, Montgomery L, Wilson KS, Ozaygen A, Brookes-Kenworthy C (2020) Evaluating the impact of open access policies on research institutions. eLife 9. https://doi.org/10.7554/elife.57067

Hwang GJ, Tsai CC(2011) Research trends in mobile and ubiquitous learning: a review of publications in selected journals from 2001 to 2010 Br J Educ Technol 42:E65–E70. https://doi.org/10.1111/j.1467-8535.2011.01183.x

Hwang GJ, Wu PH, Zhuang YY, Huang YM(2013) Effects of the inquiry-based mobile learning model on the cognitive load and learning achievement of students Interact Learn Environ 21:338–354. https://doi.org/10.1080/10494820.2011.575789

Jiang S, Ning CF (2022) Interactive communication in the process of physical education: are social media contributing to the improvement of physical training performance. Universal Access Inf Soc, 1–10. https://doi.org/10.1007/s10209-022-00911-w

Jing Y, Zhao L, Zhu KK, Wang H, Wang CL, Xia Q(2023) Research landscape of adaptive learning in education: a bibliometric study on research publications from 2000 to 2022 Sustainability 15:3115–3115. https://doi.org/10.3390/su15043115

Jing Y, Wang CL, Chen Y, Wang H, Yu T, Shadiev R (2023b) Bibliometric mapping techniques in educational technology research: a systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6

Krishnamurthy S (2020) The future of business education: a commentary in the shadow of the Covid-19 pandemic. J Bus Res. https://doi.org/10.1016/j.jbusres.2020.05.034

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40

Kyza EA, Georgiou Y(2018) Scaffolding augmented reality inquiry learning: the design and investigation of the TraceReaders location-based, augmented reality platform Interact Learn Environ 27:211–225. https://doi.org/10.1080/10494820.2018.1458039

Laurillard D(2008) Technology enhanced learning as a tool for pedagogical innovation J Philos Educ 42:521–533. https://doi.org/10.1111/j.1467-9752.2008.00658.x

Li M, Yu Z (2023) A systematic review on the metaverse-based blended English learning. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.1087508

Luo H, Li G, Feng Q, Yang Y, Zuo M (2021) Virtual reality in K-12 and higher education: a systematic review of the literature from 2000 to 2019. J Comput Assist Learn. https://doi.org/10.1111/jcal.12538

Margaryan A, Littlejohn A, Vojt G(2011) Are digital natives a myth or reality? University students’ use of digital technologies Comput Educ 56:429–440. https://doi.org/10.1016/j.compedu.2010.09.004

McMillan S(1996) Literacy and computer literacy: definitions and comparisons Comput Educ 27:161–170. https://doi.org/10.1016/s0360-1315(96)00026-7

Mo CY, Wang CL, Dai J, Jin P (2022) Video playback speed influence on learning effect from the perspective of personalized adaptive learning: a study based on cognitive load theory. Front Psychology 13. https://doi.org/10.3389/fpsyg.2022.839982

Moorhouse BL (2021) Beginning teaching during COVID-19: newly qualified Hong Kong teachers’ preparedness for online teaching. Educ Stud 1–17. https://doi.org/10.1080/03055698.2021.1964939

Moorhouse BL, Wong KM (2021) The COVID-19 Pandemic as a catalyst for teacher pedagogical and technological innovation and development: teachers’ perspectives. Asia Pac J Educ 1–16. https://doi.org/10.1080/02188791.2021.1988511

Moskal P, Dziuban C, Hartman J (2013) Blended learning: a dangerous idea? Internet High Educ 18:15–23

Mughal MY, Andleeb N, Khurram AFA, Ali MY, Aslam MS, Saleem MN (2022) Perceptions of teaching-learning force about Metaverse for education: a qualitative study. J. Positive School Psychol 6:1738–1745

Mustapha I, Thuy Van N, Shahverdi M, Qureshi MI, Khan N (2021) Effectiveness of digital technology in education during COVID-19 pandemic. a bibliometric analysis. Int J Interact Mob Technol 15:136

Nagle J (2018) Twitter, cyber-violence, and the need for a critical social media literacy in teacher education: a review of the literature. Teach Teach Education 76:86–94

Nazare J, Woolf A, Sysoev I, Ballinger S, Saveski M, Walker M, Roy D (2022) Technology-assisted coaching can increase engagement with learning technology at home and caregivers’ awareness of it. Comput Educ 188:104565

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of simulation & gaming to the literature, 1970-2019: a bibliometric review. Simul Gaming 104687812094156. https://doi.org/10.1177/1046878120941569

Nygren H, Nissinen K, Hämäläinen R, Wever B(2019) Lifelong learning: formal, non-formal and informal learning in the context of the use of problem-solving skills in technology-rich environments Br J Educ Technol 50:1759–1770. https://doi.org/10.1111/bjet.12807

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906

Pan SL, Zhang S(2020) From fighting COVID-19 pandemic to tackling sustainable development goals: an opportunity for responsible information systems research Int J Inf Manage 55:102196. https://doi.org/10.1016/j.ijinfomgt.2020.102196

Pan X, Yan E, Cui M, Hua W(2018) Examining the usage, citation, and diffusion patterns of bibliometric mapping software: a comparative study of three tools J Informetr 12:481–493. https://doi.org/10.1016/j.joi.2018.03.005

Parris Z, Cale L, Harris J, Casey A (2022) Physical activity for health, covid-19 and social media: what, where and why?. Movimento, 28. https://doi.org/10.22456/1982-8918.122533

Pasquini LA, Evangelopoulos N (2016) Sociotechnical stewardship in higher education: a field study of social media policy documents. J Comput High Educ 29:218–239

Pérez-Sanagustín M, Hernández-Leo D, Santos P, Delgado Kloos C, Blat J(2014) Augmenting reality and formality of informal and non-formal settings to enhance blended learning IEEE Trans Learn Technol 7:118–131. https://doi.org/10.1109/TLT.2014.2312719

Pinto M, Leite C (2020) Digital technologies in support of students learning in Higher Education: literature review. Digital Education Review 343–360. https://doi.org/10.1344/der.2020.37.343-360

Pires F, Masanet MJ, Tomasena JM, Scolari CA(2022) Learning with YouTube: beyond formal and informal through new actors, strategies and affordances Convergence 28(3):838–853. https://doi.org/10.1177/1354856521102054

Pritchard A (1969) Statistical bibliography or bibliometrics 25:348

Romero M, Romeu T, Guitert M, Baztán P (2021) Digital transformation in higher education: the UOC case. In ICERI2021 Proceedings (pp. 6695–6703). IATED https://doi.org/10.21125/iceri.2021.1512

Romero-Hall E, Jaramillo Cherrez N (2022) Teaching in times of disruption: faculty digital literacy in higher education during the COVID-19 pandemic. Innovations in Education and Teaching International 1–11. https://doi.org/10.1080/14703297.2022.2030782

Rospigliosi PA(2023) Artificial intelligence in teaching and learning: what questions should we ask of ChatGPT? Interactive Learning Environments 31:1–3. https://doi.org/10.1080/10494820.2023.2180191

Salas-Pilco SZ, Yang Y, Zhang Z(2022) Student engagement in online learning in Latin American higher education during the COVID-19 pandemic: a systematic review. Br J Educ Technol 53(3):593–619. https://doi.org/10.1111/bjet.13190

Selwyn N(2009) The digital native-myth and reality In Aslib proceedings 61(4):364–379. https://doi.org/10.1108/00012530910973776

Selwyn N(2012) Making sense of young people, education and digital technology: the role of sociological theory Oxford Review of Education 38:81–96. https://doi.org/10.1080/03054985.2011.577949

Selwyn N, Facer K(2014) The sociology of education and digital technology: past, present and future Oxford Rev Educ 40:482–496. https://doi.org/10.1080/03054985.2014.933005

Selwyn N, Banaji S, Hadjithoma-Garstka C, Clark W(2011) Providing a platform for parents? Exploring the nature of parental engagement with school Learning Platforms J Comput Assist Learn 27:314–323. https://doi.org/10.1111/j.1365-2729.2011.00428.x

Selwyn N, Aagaard J (2020) Banning mobile phones from classrooms-an opportunity to advance understandings of technology addiction, distraction and cyberbullying. Br J Educ Technol 52. https://doi.org/10.1111/bjet.12943

Selwyn N, O’Neill C, Smith G, Andrejevic M, Gu X (2021) A necessary evil? The rise of online exam proctoring in Australian universities. Media Int Austr 1329878X2110058. https://doi.org/10.1177/1329878x211005862

Selwyn N, Pangrazio L, Nemorin S, Perrotta C (2019) What might the school of 2030 be like? An exercise in social science fiction. Learn, Media Technol 1–17. https://doi.org/10.1080/17439884.2020.1694944

Selwyn, N (2016) What works and why?* Understanding successful technology enabled learning within institutional contexts 2016 Final report Appendices (Part B). Monash University Griffith University

Sjöberg D, Holmgren R (2021) Informal workplace learning in swedish police education-a teacher perspective. Vocations and Learning. https://doi.org/10.1007/s12186-021-09267-3

Strotmann A, Zhao D (2012) Author name disambiguation: what difference does it make in author-based citation analysis? J Am Soc Inf Sci Technol 63:1820–1833

Article   CAS   Google Scholar  

Sutherland R, Facer K, Furlong R, Furlong J(2000) A new environment for education? The computer in the home. Comput Educ 34:195–212. https://doi.org/10.1016/s0360-1315(99)00045-7

Szeto E, Cheng AY-N, Hong J-C(2015) Learning with social media: how do preservice teachers integrate YouTube and Social Media in teaching? Asia-Pac Educ Res 25:35–44. https://doi.org/10.1007/s40299-015-0230-9

Tang E, Lam C(2014) Building an effective online learning community (OLC) in blog-based teaching portfolios Int High Educ 20:79–85. https://doi.org/10.1016/j.iheduc.2012.12.002

Taskin Z, Al U(2019) Natural language processing applications in library and information science Online Inf Rev 43:676–690. https://doi.org/10.1108/oir-07-2018-0217

Tegtmeyer K, Ibsen L, Goldstein B(2001) Computer-assisted learning in critical care: from ENIAC to HAL Crit Care Med 29:N177–N182. https://doi.org/10.1097/00003246-200108001-00006

Article   CAS   PubMed   Google Scholar  

Timotheou S, Miliou O, Dimitriadis Y, Sobrino SV, Giannoutsou N, Cachia R, Moné AM, Ioannou A(2023) Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: a literature review. Educ Inf Technol 28(6):6695–6726. https://doi.org/10.1007/s10639-022-11431-8

Trujillo Maza EM, Gómez Lozano MT, Cardozo Alarcón AC, Moreno Zuluaga L, Gamba Fadul M (2016) Blended learning supported by digital technology and competency-based medical education: a case study of the social medicine course at the Universidad de los Andes, Colombia. Int J Educ Technol High Educ 13. https://doi.org/10.1186/s41239-016-0027-9

Turin O, Friesem Y(2020) Is that media literacy?: Israeli and US media scholars’ perceptions of the field J Media Lit Educ 12:132–144. https://doi.org/10.1007/s11192-009-0146-3

Van Eck NJ, Waltman L (2019) VOSviewer manual. Universiteit Leiden

Vratulis V, Clarke T, Hoban G, Erickson G(2011) Additive and disruptive pedagogies: the use of slowmation as an example of digital technology implementation Teach Teach Educ 27:1179–1188. https://doi.org/10.1016/j.tate.2011.06.004

Wang CL, Dai J, Xu LJ (2022) Big data and data mining in education: a bibliometrics study from 2010 to 2022. In 2022 7th International Conference on Cloud Computing and Big Data Analytics ( ICCCBDA ) (pp. 507-512). IEEE. https://doi.org/10.1109/icccbda55098.2022.9778874

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023) Understanding the continuance intention of college students toward new E-learning spaces based on an integrated model of the TAM and TTF. Int J Hum-Comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609

Wong L-H, Boticki I, Sun J, Looi C-K(2011) Improving the scaffolds of a mobile-assisted Chinese character forming game via a design-based research cycle Comput Hum Behav 27:1783–1793. https://doi.org/10.1016/j.chb.2011.03.005

Wu R, Yu Z (2023) Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. Br J Educ Technol. https://doi.org/10.1111/bjet.13334

Yang D, Zhou J, Shi D, Pan Q, Wang D, Chen X, Liu J (2022) Research status, hotspots, and evolutionary trends of global digital education via knowledge graph analysis. Sustainability 14:15157–15157. https://doi.org/10.3390/su142215157

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.3390/su142215157

Yu Z (2022) Sustaining student roles, digital literacy, learning achievements, and motivation in online learning environments during the COVID-19 pandemic. Sustainability 14:4388. https://doi.org/10.3390/su14084388

Za S, Spagnoletti P, North-Samardzic A(2014) Organisational learning as an emerging process: the generative role of digital tools in informal learning practices Br J Educ Technol 45:1023–1035. https://doi.org/10.1111/bjet.12211

Zhang X, Chen Y, Hu L, Wang Y (2022) The metaverse in education: definition, framework, features, potential applications, challenges, and future research topics. Front Psychol 13:1016300. https://doi.org/10.3389/fpsyg.2022.1016300

Zhou M, Dzingirai C, Hove K, Chitata T, Mugandani R (2022) Adoption, use and enhancement of virtual learning during COVID-19. Education and Information Technologies. https://doi.org/10.1007/s10639-022-10985-x

Download references

Acknowledgements

This research was supported by the Zhejiang Provincial Social Science Planning Project, “Mechanisms and Pathways for Empowering Classroom Teaching through Learning Spaces under the Strategy of High-Quality Education Development”, the 2022 National Social Science Foundation Education Youth Project “Research on the Strategy of Creating Learning Space Value and Empowering Classroom Teaching under the background of ‘Double Reduction’” (Grant No. CCA220319) and the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202310337023).

Author information

Authors and affiliations.

College of Educational Science and Technology, Zhejiang University of Technology, Zhejiang, China

Chengliang Wang, Xiaojiao Chen, Yidan Liu & Yuhui Jing

Graduate School of Business, Universiti Sains Malaysia, Minden, Malaysia

Department of Management, The Chinese University of Hong Kong, Hong Kong, China

College of Humanities and Social Sciences, Beihang University, Beijing, China

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: Y.J., C.W.; methodology, C.W.; software, C.W., Y.L.; writing-original draft preparation, C.W., Y.L.; writing-review and editing, T.Y., Y.L., C.W.; supervision, X.C., T.Y.; project administration, Y.J.; funding acquisition, X.C., Y.L. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

Corresponding author

Correspondence to Yuhui Jing .

Ethics declarations

Ethical approval.

Ethical approval was not required as the study did not involve human participants.

Informed consent

Informed consent was not required as the study did not involve human participants.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wang, C., Chen, X., Yu, T. et al. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11 , 256 (2024). https://doi.org/10.1057/s41599-024-02717-y

Download citation

Received : 11 July 2023

Accepted : 17 January 2024

Published : 12 February 2024

DOI : https://doi.org/10.1057/s41599-024-02717-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A meta-analysis of learners’ continuance intention toward online education platforms.

• Chengliang Wang

Education and Information Technologies (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

ChatGPT in higher education - a synthesis of the literature and a future research agenda

• Open access
• Published: 02 May 2024

Cite this article

You have full access to this open access article

• Pritpal Singh Bhullar 1 ,
• Mahesh Joshi 2 &
• Ritesh Chugh   ORCID: orcid.org/0000-0003-0061-7206 3  

1571 Accesses

33 Altmetric

Explore all metrics

ChatGPT has emerged as a significant subject of research and exploration, casting a critical spotlight on teaching and learning practices in the higher education domain. This study examines the most influential articles, leading journals, and productive countries concerning citations and publications related to ChatGPT in higher education, while also shedding light on emerging thematic and geographic clusters within research on ChatGPT’s role and challenges in teaching and learning at higher education institutions. Forty-seven research papers from the Scopus database were shortlisted for bibliometric analysis. The findings indicate that the use of ChatGPT in higher education, particularly issues of academic integrity and research, has been studied extensively by scholars in the United States, who have produced the largest volume of publications, alongside the highest number of citations. This study uncovers four distinct thematic clusters (academic integrity, learning environment, student engagement, and scholarly research) and highlights the predominant areas of focus in research related to ChatGPT in higher education, including student examinations, academic integrity, student learning, and field-specific research, through a country-based bibliographic analysis. Plagiarism is a significant concern in the use of ChatGPT, which may reduce students’ ability to produce imaginative, inventive, and original material. This study offers valuable insights into the current state of ChatGPT in higher education literature, providing essential guidance for scholars, researchers, and policymakers.

Similar content being viewed by others

Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

Examining science education in chatgpt: an exploratory study of generative artificial intelligence.

Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation

Avoid common mistakes on your manuscript.

1 Introduction

ChatGPT, or Chat Generative Pre-trained Transformer, is a popular generative Artificial Intelligence (AI) chatbot developed by OpenAI, employing natural language processing to deliver interactive human-like conversational experiences (Jeon et al., 2023 ; Angelis et al., 2023 ). ChatGPT utilises a pre-trained language learning model, derived from an extensive big-data corpus, to predict outcomes based on a given prompt (Crawford et al., 2023 ; Geerling et al., 2023 ; Li et al., 2023 ). Since its inception, ChatGPT has attracted widespread attention and popularity and has the potential to disrupt the education sector (Rana, 2023 ). According to a research survey of adults conducted by the Pew Research Centre, approximately 60% of adults in the United States and 78% of adults in Asia possess knowledge of ChatGPT; furthermore, men are more familiar with ChatGPT than women (Vogels, 2023 ). The study also found that among ethnic groups globally, individuals of Asian descent have the highest level of familiarity with AI-based large language models (LLMs).

People have found value in using ChatGPT for a wide range of purposes, including generating creative content, answering questions, providing explanations, offering suggestions, and even having casual conversations (Crawford et al., 2023 ; Throp, 2023 ; Wu et al., 2023 ). Furthermore, ChatGPT is an effective digital assistant for facilitating a thorough understanding of diverse and intricate subjects using simple and accessible language. Given these features, ChatGPT has the potential to bring about a paradigm shift in traditional methods of delivering instruction and revolutionise the future of education (Tlili et al., 2023 ). ChatGPT stands out as a promising tool for open education, enhancing the independence and autonomy of autodidactic learners through personalised support, guidance, and feedback, potentially fostering increased motivation and engagement (Firat, 2023 ). Its capabilities encompass facilitating complex learning, asynchronous communication, feedback provision, and cognitive offloading (Memarian & Doleck, 2023 ).

However, the rapid expansion of ChatGPT has also aroused apprehensions in the academic world, particularly after reports surfaced that the New York Department of Education had unexpectedly imposed a ban on access to the tool due to concerns about academic integrity violations (Sun et al., 2023 ; Neumann et al., 2023 ; Crawford et al., 2023 ). Students who use ChatGPT to produce superior written assignments may have an unfair advantage over peers who lack access (Farrokhnia et al., 2023 ; Cotton et al., 2023 ). Ethical concerns about the deployment of LLMs include the potential for bias, effects on employment, misuse and unethical deployment, and loss of integrity. However, there has been little research on the potential dangers that a sophisticated chatbot such as ChatGPT poses in the realm of higher education, particularly through the lens of a systematic literature review and bibliometric techniques.

In this light, this paper explores the literature on the application of ChatGPT in higher education institutions and the obstacles encountered in various disciplines from the perspectives of both faculty and students. The paper aims to analyse the current state of the field by addressing the following overarching research questions using bibliographic coupling, co-occurrence analysis, citation analysis, and co-authorship analysis:

What are the most influential articles in terms of citations in research related to ChatGPT in education?

What are the top journals and countries in terms of publication productivity related to the implications of ChatGPT in higher education institutions?

What are the emerging thematic clusters in research on the role and challenges of ChatGPT in teaching and learning in higher education institutions?

What are the geographic clusters in research on the role and challenges of ChatGPT in teaching and learning in higher education institutions?

2 Methodology

In conducting this study, publications on the impact of ChatGPT on various aspects of higher education institutions were systematically identified through an extensive search using Elsevier’s Scopus database, a comprehensive repository hosting over 20,000 globally ranked, peer-reviewed journals (Mishra et al., 2017 ; Palomo et al., 2017 ; Vijaya & Mathur, 2023 ). Scopus is a widely used database for bibliometric analyses and is considered one of the “largest curated databases covering scientific journals” (pg. 5116) in different subject areas (Singh et al., 2021 ). Widely acclaimed for its comprehensive coverage, Scopus has been extensively employed in bibliometric analyses across diverse disciplines, as evidenced by studies in capital structure theories, business research, entrepreneurial orientation and blockchain security (Bajaj et al., 2020 ; Donthu et al., 2020 ; Gupta et al., 2021 ; Patrício & Ferreira, 2020 ). Notably, despite the “extremely high” correlation between the Web of Science and Scopus databases, Scopus’s status as a superior and versatile data source for literature extraction is reinforced by its broader coverage of subject areas and categories compared to the narrower journal scope of Web of Science, facilitating scholars in locating literature most pertinent to the review area (Archambault et al., 2009 ; Paul et al., 2021 ). To ensure a systematic literature review, we adhered to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines (Page et al., 2021 ) for the search, identification, selection, reading, and data extraction from the articles retrieved through the Scopus database (Fig.  1 ). Reliance on a single database is acceptable within the PRISMA framework (Moher et al., 2009 ).

Employing Boolean-assisted search queries, we aimed to capture a comprehensive range of topics related to ChatGPT’s impact on higher education institutions. Specific search queries were carefully selected to ensure a broad yet relevant search scope and included the following:

“ChatGPT and Teaching learning in universities” OR “Effect of ChatGPT in higher education institution” OR “ChatGPT and student assessment in higher education” OR “ChatGPT and academic integrity” OR “ChatGPT and teaching pedagogy in higher education institution” OR “ChatGPT and cheating student course assignment” OR “ChatGPT and teaching in higher education” OR “Implications of ChatGPT in higher education institutions” OR “ChatGPT and evaluation criteria in higher education institution” OR “ChatGPT in universities” OR “ChatGPT and student learnings. ”

The study includes papers published and included in the Scopus database on or before May 26, 2023 on the theme of ChatGPT and higher education. This timeframe was chosen to encompass the most recent and relevant literature available up to the point of data retrieval. Papers identified through the search queries underwent inclusion or exclusion based on predetermined criteria. Specifically, only papers published in journals were considered for this study, as these undergo a peer-review process and are subject to stringent selection criteria set by the journals, ensuring their quality and reliability. Papers in conference proceedings were excluded from the start of the search. Only papers written in English were included to maintain consistency and clarity, whereas others were excluded. Of the 48 research papers that were initially identified, 47 were ultimately selected for the bibliometric analysis, which was conducted using VOSviewer, a bibliometric analysis tool.

PRISMA Flowchart

From the identified pool of 47 articles, the analysis uncovered a nuanced distribution of research methodologies. Specifically, 11 studies were grounded in quantitative research methodologies, underscoring a quantitative focus within the literature. In contrast, a substantial majority of 31 articles embraced a qualitative framework, showcasing a diverse spectrum that included pure qualitative research, editorials, letters to the editor, and opinion pieces. Furthermore, the review brought to light four literature reviews, signifying a synthesis of existing knowledge, and identified one study that strategically employed a mixed-methods approach, blending both qualitative and quantitative research techniques.

To address the research questions, the selected publications underwent analysis using various bibliometric techniques. For the first and second research questions, citation analysis was employed. For the third and fourth research questions, bibliographic analysis was performed in VOSviewer software to generate clusters.

3 Findings and discussion

3.1 publication trend.

Information from the Scopus database indicates that academics began focusing on investigating various aspects of ChatGPT’s potential in higher education in 2022, as they published their findings in 2023. All academic articles in reputable publications in the Scopus database were published in 2023.

3.2 Citation analysis

Table  1 presents the top ten articles according to the number of citations. The number of articles increased significantly in 2023, consistent with the emerging nature and growing relevance of the topic. Exploring the ramifications of ChatGPT in higher education is a recent focal point for scholars, with numerous aspects warranting deeper investigation. The limited citation count, as anticipated, underscores that publications from 2023 are in the early stages of gaining visibility and recognition within the academic community.

The article by Thorp ( 2023 ), entitled “ChatGPT is fun, but not an author”, has received the highest number of citations (79). Thorp stresses the risks associated with implementing ChatGPT in the classroom. Although ChatGPT is an innovative AI tool, significant barriers remain to its implementation in the field of education. According to Thorp, using ChatGPT in academic writing is still inefficient. Thorp also expresses concerns about the rising prevalence of ChatGPT in the fabrication of scientific publications. The second most-cited work, “How Does ChatGPT Perform on the United States Medical Licensing Examination?” by Gilson and colleagues, has received 27 citations. Gilson et al. ( 2023 ) evaluated the accuracy, speed and clarity of ChatGPT’s responses to questions on the United States Medical Licensing Examination’s Step 1 and Step 2 tests. The text responses generated by ChatGPT were evaluated using three qualitative metrics: the logical justification of the chosen answer, the inclusion of information relevant to the question, and the inclusion of information extraneous to the question. The model attained a level of proficiency comparable to that of a third-year medical student. The study demonstrates the potential utility of ChatGPT as an interactive educational resource in the field of medicine to facilitate the acquisition of knowledge and skills. Third is Kasneci et al.’s article “ChatGPT for good? On opportunities and challenges of large language models for education”, with 13 citations. This paper examines the benefits and drawbacks of using language models in the classroom from the perspectives of both teachers and students. The authors find that these comprehensive language models can serve as a supplement rather than a replacement for classroom instruction. Each of the remaining top-ten articles mentioned the impact of ChatGPT on academic integrity in education and had received fewer than ten citations at the time of analysis.

Table  2 presents the top 10 journals in terms of the number of citations of publications related to the topic of ChatGPT in higher education. The journal Science , which published “ChatGPT is fun, but not an author,” was deemed most influential because it received the highest number of citations (79). JMIR Medical Education has published two articles that have been cited by 30 other research articles on the same topic. Journal of University Teaching and Learning Practise has published the most articles: three. Innovations in Education and Teaching International has published two articles on this topic, which together have been cited by six articles.

As shown in Table  3 , the majority of research articles pertaining to ChatGPT and higher education have originated from countries in Asia. Six of the top 10 countries for publishing articles on this topic are located in the Asian continent. However, the most influential studies in terms of citations have been produced by the United States, Germany, Australia, and the United Kingdom. Combined, these countries have received a total of 63 citations, with individual counts of 36, 17, 7, and 7, respectively. These four countries have 90% of the total citations of the top 10 most productive countries in the field of research on higher education perspectives on ChatGPT.

3.3 Bibliographic coupling

3.3.1 thematic clusters.

Four thematic clusters (TCs) were identified from the included research articles, as shown in Table  4 . VOSviewer was used to perform clustering based on bibliographic coupling. This method identifies relations between documents by examining publications that cite the same sources (Boyack & Klavans, 2010 ). VOSviewer clusters articles with a common knowledge base, assigning each publication to exactly one cluster. To implement this clustering technique, we assessed the co-occurrence of bibliographic references among articles within our dataset. Co-occurrence was determined by identifying shared references between articles, indicating a thematic connection (Boyack & Klavans, 2010 ). Articles sharing common references were considered to co-occur, enabling us to quantify the extent of thematic relationships based on the frequency of shared references. We identified and categorised thematic clusters within our dataset through the combined approach of VOSviewer clustering and co-occurrence analysis. This method typically results in a distribution of clusters, with a limited number of larger clusters and a more substantial number of smaller clusters.

The clusters were derived through an analysis of subordinate articles extracted from the Scopus database. VOSviewer systematically organised similar articles into distinct clusters based on the shared patterns of bibliographic references (Van Eck & Waltman, 2010 ). To ensure methodological transparency and robustness, we established clear criteria and parameters for clustering. Specifically, keywords with a minimum frequency ( n  = 5) were included in the analysis, and co-occurrence was calculated based on a pairwise comparison method. This systematic approach ensured the meaningful representation of thematic relationships within the dataset, guided by insights from previous literature (Jarneving, 2007 ). Using cluster analysis techniques, the articles were organised into cohesive groups characterised by the degree of thematic homogeneity guided by the nature of the research findings. This approach ensured a robust representation of the underlying thematic structure (Jarneving, 2007 ).

Furthermore, to mitigate the risk of subjective bias in thematic categorisation, a counter-coding approach was employed. A second researcher independently categorised thematic clusters identified by VOSviewer to assess inter-rater agreement. The level of agreement between the two researchers was assessed using Cohen’s kappa coefficient, ensuring the reliability and validity of the thematic classification process. The resulting kappa coefficient (0.69) indicated substantial agreement, suggesting a high level of agreement beyond what would be expected by chance alone (Gisev et al., 2013 ). Furthermore, the nomenclature assigned to each cluster was finalised based on the predominant research theme emerging from the analysis, providing a concise and informative label for each group.

TC1: ChatGPT and Academic Integrity: Cotton et al. ( 2023 ) describe ChatGPT as a double-edged sword that potentially threatens academic integrity. AI essay writing systems are programmed to churn out essays based on specific guidelines or prompts, and it can be difficult to distinguish between human and machine-generated writing. Thus, students could potentially use these systems to cheat by submitting essays that are not their original work (Dehouche, 2021 ). Kasneci et al. ( 2023 ) argue that effective pedagogical practices must be developed in order to implement large language models in classrooms. These skills include not only a deep understanding of the technology but also an appreciation of its constraints and the vulnerability of complex systems in general. In addition, educational institutions need to develop a clearly articulated plan for the successful integration and optimal use of big language models in educational contexts and teaching curricula. In addition, students need to be taught how to verify information through a teaching strategy emphasising critical thinking effectively. Possible bias in the generated output, the need for continuous human supervision, and the likelihood of unforeseen effects are just a few of the challenges that come with the employment of AI systems. Continuous monitoring and transparency are necessary to ensure academic integrity while using ChatGPT. Lim et al. ( 2023 ) report that ChatGPT poses academic integrity challenges for the faculty of higher education institutions, who must verify whether academic work (assignments, research reports, etc.) submitted by students is derived from the fresh perspective of data analysis or plagiarised and recycled (copying and pasting original work) by ChatGPT. ChatGPT may threaten student learning and classroom engagement if students have access to information and course assignments without assessing their integrity. Perkins ( 2023 ) also expresses concerns regarding academic integrity in the use of ChatGPT. Students are utilising ChatGPT to complete their course assignments without attribution rather than producing original work. Higher education institutions must establish clear boundaries regarding academic integrity and plagiarism in light of the growing utilisation of AI tools in academic and research settings. In addition, the challenges posed by AI essay writing systems like ChatGPT necessitate a multifaceted approach to safeguard academic integrity. Educational institutions should invest in comprehensive educational programs that not only teach students the ethical use of technology but also incorporate rigorous assessments of critical thinking skills. Additionally, integrating AI literacy into the curriculum, with a focus on understanding the limitations and potential biases of big language models, can empower students to discern between human and machine-generated content.

TC2: ChatGPT and Learning Environment: According to Crawford et al. ( 2023 ), increased stress levels and peer pressure among university students have created a favourable environment for the use of AI tools. ChatGPT provides enhanced educational opportunities for college-level students. It can help students identify areas they may have overlooked, offer guidance on additional reading materials, and enhance existing peer and teacher connections. In addition, ChatGPT can propose alternative methods of evaluating students beyond conventional assignments. Crawford et al. ( 2023 ) recommend providing practical assignments incorporating ChatGPT as a supplementary tool to reduce plagiarism. Su ( 2023 ) documents that ChatGPT can provide students with a personalised learning experience based on their specific needs. In addition, the ChatGPT platform can be used to create a virtual coaching system that offers prompt feedback to educators during their classroom evaluations. This approach fosters critical thinking and supports early childhood educators in refining their teaching methodologies to optimise interactive learning outcomes for students. Tang ( 2023b ) proposes that bolstering research integrity can be achieved by imposing restrictions on the utilisation of NLP-generated content in research papers. Additionally, the author advocates for transparency from researchers, emphasising the importance of explicitly stating the proportion of NLP-generated content incorporated in their papers. This recommendation prompts a critical examination of the role of AI-generated content in scholarly work, emphasising the importance of nurturing independent research and writing skills for both students and researchers.

TC3: ChatGPT and Student Engagement: Lee ( 2023 ) examines the ability of ChatGPT to provide an interactive learning experience and boost student engagement beyond textbook pedagogy. Iskender ( 2023 ) explains that ChatGPT provides a mechanism for students to generate and investigate diverse concepts expeditiously, thereby helping them engage in imaginative and evaluative thinking on specific subject matter. This approach has the potential to optimise time management for students and allow them to concentrate on more advanced cognitive activities. AI tools such as ChatGPT can potentially enhance the personalisation of learning materials by providing visual aids and summaries that can aid the learning process and significantly improve students’ competencies. Hence, leveraging ChatGPT in education can revolutionise learning by facilitating interactive experiences, nurturing imaginative thinking, and optimising time management for students.

TC4: ChatGPT and Scholarly Research: Ivanov and Soliman ( 2023 ) and Yan ( 2023 ) focus on the practical applications and implications of LLMs like ChatGPT in educational settings and scholarly research within the context of language learning, writing, and tourism. Yan’s investigation into ChatGPT’s application in second-language writing examines its effectiveness in addressing specific writing tasks at the undergraduate level. The findings underscore the nuanced balance between the strengths of ChatGPT and the inherent limitations in handling demanding academic writing tasks. Nevertheless, ChatGPT is also labelled as an ‘all-in-one’ solution for scholarly research and writing (Yan, 2023 ). In parallel, Ivanov and Soliman ( 2023 ) highlight that ChatGPT can assist scholars in the field of tourism research by composing preliminary literature reviews, substantiating their chosen methodologies, and creating visual aids such as tables and charts. Furthermore, the researchers outline that ChatGPT could provide valuable methodological ideas and insights by helping researchers generate questions and corresponding scales for inclusion in questionnaires. Hence, ChatGPT has the potential to become a valuable ally as a facilitator in academic writing processes and has the potential to transform the research workflow.

3.3.2 Geographic clusters

The results of the country-based bibliographic analysis are summarised in Table  5 . The present study utilised the prevailing research theme in the existing literature as a framework for categorising the countries into four distinct clusters on the basis of the number of documents published from different countries.

Cluster 1: Implications of ChatGPT for Student Examinations and Education : Cluster 1 is composed of five countries: Germany, Ireland, South Korea, Taiwan, and the United States. Researchers in these countries have emphasised the potential role of ChatGPT in higher education within the context of AI language models. Eleven research articles related to this theme were published by researchers based in the United States, the most in this cluster. The top three articles in Table  1 are from the United States. The study entitled “Opportunities and Challenges of Large Language Models for Education,” was authored by German researchers (Kasneci et al., 2023 ) and has been widely cited in the academic community (13 citations). The remaining studies were conducted by researchers from South Korea and Taiwan and focused on the impact of ChatGPT on the education sector and its associated opportunities and challenges. This cluster demonstrates that students could benefit greatly from using ChatGPT in performing various academic tasks, such as reviewing and revising their work, verifying the accuracy of homework answers, and improving the quality of their essays. It has also aided postgraduates whose first language is not English improve their writing, as ChatGPT can be instructed to rewrite a paragraph in a scholarly tone from scratch. The outcomes have demonstrated significant efficacy, thereby alleviating the cognitive load associated with translation for these students, enabling them to concentrate on the substance of their writing rather than the intricacies of composing in an unfamiliar language. To harness the potential benefits, future research could focus on developing targeted training programs for students and educators that emphasise the effective utilisation of ChatGPT to enhance not only academic tasks but also language proficiency for non-native English speakers, addressing both cognitive load and language intricacies.

Cluster 2: ChatGPT and Academic Integrity : Cluster 2 comprises research studies conducted by authors from Japan, Bangladesh, Hong Kong, Nigeria, Pakistan, UAE, the UK, Vietnam and the Netherlands. The most influential study in this cluster, “Unlocking the power of ChatGPT: A framework for applying Generative AI in education”, was authored by researchers from Hong Kong (Su & Yang, 2023 ). They document that ChatGPT can be used to respond to student inquiries, reducing the time and effort required of educators and allowing them to focus their resources on other activities, such as scholarly investigations. Farrokhnia et al. ( 2023 ) and Yeadon et al. ( 2023 ) state that ChatGPT can write scientific abstracts with fabricated data and essays that can evade detection by reviewers. According to Liebrenz et al. ( 2023 ), ChatGPT tends to produce erroneous and incoherent responses, thereby raising the potential for disseminating inaccurate information in scholarly literature. The higher-order cognitive abilities of ChatGPT are relatively low, especially in areas related to creativity, critical thinking, reasoning, and problem-solving. ChatGPT could reduce students’ motivation to explore topics independently, draw their own conclusions, and solve problems independently (Kasneci et al., 2023 ). Ibrahim et al. ( 2023 ) find that ChatGPT can engage students in their academic pursuits. ChatGPT can enhance the writing abilities of non-native English speakers to allow them to concentrate on higher-order cognitive processes. This technological development allows faculty members to allocate more attention to conceptualisation and writing rather than focusing on the mechanics of grammar and spelling. However, there is a debate among intellectuals regarding the implications of AI for content creation, with some asserting that it detracts from innovative content development. The possibility that ChatGPT threatens academic honesty by facilitating essay plagiarism is being acknowledged. In addition, in the absence of appropriate citations, this textual content may violate copyright regulations. Cotton et al. ( 2023 ) express concerns about the potential impact of ChatGPT on academic integrity and plagiarism. Their work corroborates Dehouche’s ( 2021 ) assertion that students may use ChatGPT to engage in academic dishonesty by submitting essays that are not their original work. According to Cotton et al. ( 2023 ), ChatGPT users have a competitive advantage over non-users and can achieve higher grades on their coursework assignments by utilising the AI-based language tool. They classify ChatGPT as a versatile instrument with the potential to pose a threat to academic integrity, noting that AI essay writing systems are specifically programmed to generate content based on specific parameters or prompts, thereby challenging the discernment between human-authored and machine-generated content. Distinguishing between the academic work produced by students and the content of ChatGPT when evaluating assignments is a significant challenge for faculty. It is recommended that academic staff continually monitor student assignments for academic misconduct infractions, coupled with transparent communication about the potential risks associated with AI-generated content.

Cluster 3: ChatGPT and Students’ Learning : Cluster 3 comprises Malaysia, China and Australia. This cluster mainly includes studies of the role of AI-based models in student learning. Researchers from Australia (Crawford et al., 2023 ; Lim et al., 2023 ; Lawrie, 2023 ; Li et al., 2023 ; Seth et al., 2023 ; Cingillioglu, 2023 ; Skavronskaya, 2023 ; and Johinke, 2023 ) have contributed the most (8 studies) to this cluster and put their weight behind the role of AI and student learning in various disciplines. One of the most influential papers, “Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators”, was authored by researchers from both Australia and Malaysia (Lim et al., 2023 ) and reflected on the role of AI in classroom learning and teaching. Rather than banning AI tools, the authors advocate for the productive use of these tools in classrooms to facilitate more engaging student learning. Another Australian study titled, “Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI)” (Crawford et al., 2023 ) highlights AI as an alternative path of learning for students. ChatGPT can promptly evaluate students’ assignments and help them identify areas of weakness. Educators have the option to provide innovative assessments to their students instead of adhering solely to conventional assessments. ChatGPT can augment pedagogical approaches, evaluation structures, and the comprehensive educational milieu by reinforcing the trilateral association among instructors, learners, and technology. The implementation of ChatGPT can provide students with a personalised and interactive learning and research experience facilitated by virtual tutors and customised recommendations. In light of the research in this cluster, the integration of ChatGPT into education should inspire a paradigm shift towards a more dynamic and personalised learning environment. Institutions can explore strategic partnerships with AI researchers to develop context-specific applications of ChatGPT that cater to diverse educational needs, promoting a symbiotic relationship between human instructors, students, and technology for an enriched learning experience.

Cluster 4: ChatGPT and Field-specific Research : This cluster includes research by authors in Asian and European countries (India, Oman, Bulgaria and New Zealand) that has emphasised the potential role of ChatGPT in the medical and tourism industries. Authors from India explored the role of ChatGPT in the medical field (Seetharaman, 2023 ; Subramani et al., 2023 ). Seetharaman ( 2023 ) reports that ChatGPT offers supplementary language assistance to students who are not proficient in English, enabling them to enhance their language proficiency and effectively communicate in English, the principal language of instruction in medical establishments. The ChatGPT platform has the potential to serve as a tool for medical students to replicate patient interactions in a simulated environment, such as accurately obtaining medical histories and documenting symptoms. According to Subramani et al. ( 2023 ), ChatGPT is a highly efficient and user-friendly AI technology that can aid healthcare professionals in various aspects, such as diagnosis, critical decision-making, and devising appropriate treatment plans. ChatGPT has demonstrated impressive performance on medical exams, indicating its potential as a valuable resource for enhancing medical education and assessment (Subramani et al., 2023 ) and can support interdisciplinarity in tourism research (Nautiyal et al., 2023 ). Ivanov and Soliman ( 2023 ) note the potential of ChatGPT to serve as a digital instructor to provide students with enhanced and effective learning experiences and outcomes. Digital instructors can impart knowledge in diverse languages and thus can be used to educate individuals of varying nationalities and backgrounds in the field of tourism. Furthermore, LLM-based chatbots, including ChatGPT, can assess written assignments and provide direction on linguistic proficiency, syntax, and composition, ultimately enhancing students’ scholarly writing proficiency. In exploring the intersection of ChatGPT with medical education, institutions can pioneer innovative approaches by using the platform to create immersive, simulated patient interactions that go beyond language assistance, allowing medical students to practice nuanced skills such as medical history gathering and symptom documentation. Simultaneously, leveraging ChatGPT as a versatile digital instructor offers a unique opportunity to provide cross-cultural and multilingual education, contributing to a more inclusive and globally competent workforce within the tourism industry.

3.4 Challenges of ChatGPT in higher education

In addition to some previously mentioned challenges, such as the potential for plagiarism, the investigation also identified other key challenges in implementing ChatGPT within the context of higher education’s teaching and learning environment. Wu and Yu ( 2023 ) found that the benefits of AI-based ChatGPT are more in higher education as compared to primary and secondary education. The study also reported that the novelty effects of AI chatbots may enhance learning outcomes in brief interventions, but their efficacy diminishes in longer interventions.

First, the implementation of ChatGPT within the educational context engenders learning impediments. In the absence of adequate monitoring and regulation, the technology could lead to human unintelligence and unlearning, but teachers will become more adaptive and create authentic assessments to enhance student learning (Alafnan et al., 2023 ; Lawrie, 2023 ). Second, the technology could be used in a manner that violates students’ privacy. If the model is not adequately secured, it could surreptitiously gather confidential data from students without their explicit awareness or authorisation (Kanseci, 2023). Third, the technology could facilitate discrimination against particular students. If the model is not trained on a dataset that accurately represents the entire student population, it has the potential to create disparities in educational access (Cingillioglu, 2023 ; Lin et al., 2023 ). Fourth, according to Ivanov and Soloman (2023), ChatGPT lacks access to real-time data. Therefore, its responses may be inconsequential, inaccurate, or outdated. The information provided in response to a specific query may also be insufficient. Gao et al. (2022) highlight the need for further investigation of the precision and scholarly authenticity of ChatGPT. Fifth, it may be difficult for ChatGPT to comprehend the context and subtleties of complex academic subjects and answer complex questions (Adetayo, 2023 ; Eysenbach, 2023 ; Neumann et al., 2023 ). The system can misinterpret inquiries, offer inadequate or inaccurate responses, or struggle to comprehend the fundamental purpose behind questions (Clark, 2023 ). In particular, ChatGPT may not have the requisite expertise in highly specialised or advanced subjects such as advanced mathematics or specific sciences. Hence, it may not deliver precise and accurate answers (Neumann et al., 2023 ; Fergus et al., 2023 ). Karaali ( 2023 ) claimed that the primary emphasis in the field of AI is currently directed towards the enhancement of advanced cognitive abilities and mental processes associated with quantitative literacy and quantitative reasoning. However, it is important to acknowledge that fundamental skills such as writing, critical thinking, and numeracy continue to serve as essential foundational components among students. Although AI is making significant progress in fundamental domains, it appears that students are experiencing a decline in performance in the context of fundamental skills. Consequently, NLP-based adaptive learner support and education require further investigation (Bauer et al., 2023 ).

In addressing the challenges of ChatGPT in education, educators need to adapt and develop authentic assessments that mitigate the risk of human unlearning, ensuring that technology enhances, rather than hinders, student learning experiences. Simultaneously, recognising the limitations of ChatGPT in comprehending the nuances of highly specialised subjects underscores the importance of balancing advancements in AI’s cognitive abilities with continued emphasis on fundamental skills like critical thinking, writing, and numeracy, urging a reevaluation of priorities in AI-driven educational research towards comprehensive learner support.

4 Conclusion, implications and agenda for future research

This study identified the most influential articles and top journals and countries in terms of citations and publication productivity related to ChatGPT in higher education, as well as highlighted emerging thematic clusters and geographic clusters in research on the role and challenges of ChatGPT in teaching and learning in higher education institutions. Articles on the topic of ChatGPT in higher education published up to May 2023 were identified by searching the Scopus database. Given the emergent nature of ChatGPT starting in late 2022, all the included articles were published in 2023. Thus, this specific research domain remains relatively unexplored. The findings of this analysis reveal that the United States is the most productive country in terms of research on the role of ChatGPT in higher education, especially relating to academic integrity and research. US researchers also emerged as the most influential in terms of number of citations in the literature. Our findings corroborate those of previous research (Crompton & Burke, 2023 ). However, 60% of the articles in our shortlisted literature emanated from Asian countries.

Four thematic clusters (academic integrity, student engagement, learning environment and research) were identified. Furthermore, the country-based bibliographic analysis revealed that research has focused on student examinations, academic integrity, student learning and field-specific research in medical and tourism education (Nautiyal et al., 2023 ; Subramani et al., 2023 ). Plagiarism is recognised as a major challenge that hinders students’ creativity, innovativeness and originality when using ChatGPT in their academic pursuits. To mitigate the potential drawbacks of using ChatGPT in educational and research settings, proactive measures should be taken to educate students and researchers alike on the nature of plagiarism, its negative impacts and academic integrity (Shoufan, 2023 ; Teixeira, 2023 ) Educators may ask students to provide a written acknowledgement of the authenticity of their assignments and their non-reliance on ChatGPT. Such an acknowledgement would discourage students from utilising ChatGPT in their academic and research endeavours and establish accountability for their academic pursuits. In addition, educators should develop authentic assessments that are ChatGPT-proof.

ChatGPT lacks emotional intelligence and empathy, both of which are crucial in effectively addressing the emotional and psychological dimensions of the learning process (Farrokhnia et al., 2023 ; Neumann et al., 2023 ). Higher education institutions may encounter challenges in using ChatGPT to deliver suitable assistance, comprehension, or direction to students needing emotional or mental health support. The significance of human interaction in learning cannot be overstated. Achieving a balance between using AI and the advantages of human guidance and mentorship is a persistent challenge that requires attention (Neumann et al., 2023 ; Rahman et al., 2023 ). Strzelecki ( 2023 ) observed in his research that behavioural intention and personal innovativeness are the two major determinants behind the adoption of ChatGPT among students.

4.1 Implications

The findings of the present study have numerous important implications. This study provides insight into the current state of ChatGPT in higher education and thus can serve as valuable guidance for academics, practitioners, and policymakers. The study’s findings contribute to the literature by providing new insights into the role of ChatGPT and strategies for mitigating its negative aspects and emphasising its positive attributes.

First, the implementation of AI in education can improve academic performance and student motivation, particularly by facilitating personalised learning. Educational institutions should monitor and regulate students’ use of such technologies proactively. Higher education institutions also ought to prioritise the training of their educators in effectively utilising AI technologies, including ChatGPT. Concurrently, it is imperative for these institutions to equip students with comprehensive academic integrity training, shedding light on the appropriate and inappropriate applications of AI tools like ChatGPT. This includes creating awareness about the potential consequences of utilising these technologies for dishonest practices. Furthermore, educational establishments need to urgently revisit and refine their academic integrity policies to address the evolving landscape shaped by the integration of artificial intelligence tools in various academic facets. This proactive approach will foster a learning environment that embraces technological advancements and upholds the principles of honesty and responsible use. Institutional regulations on accountability and transparency should guide the frameworks that govern the use of AI in the campus environment (Pechenkina, 2023 ; Sun & Hoelscher, 2023 ; Dencik & Sanchez-Monedero, 2022 ).

Second, faculty members must proactively replace traditional coursework with modern alternatives that foster elevated levels of critical thinking among students, as suggested by Zhai ( 2022 ). Educators and learners can augment the academic material produced by ChatGPT with their own insights and information obtained from credible scholarly resources (Emenike & Emenike, 2023 ).

Third, ChatGPT should not be considered a threat to the education sector but a supplementary tool for human instruction that can enhance teaching and learning. It is imperative to acknowledge that the vital role of human educators cannot be replaced (Karaali, 2023 ) Moreover, ChatGPT can potentially enhance the accessibility and inclusivity of higher education. Alternative formats, linguistic support, and individualised explanations can help students who are studying English as a second language, are not native English speakers, or have other unique learning needs. Furthermore, Alnaqbi and Fouda ( 2023 ) highlight the implications of AI in evaluating the teaching style of faculty in higher education by collecting the feedback of students through social media and ChatGPT.

Fourth, the faculty in higher education institutions could address ethical concerns by providing students with explicit and comprehensive guidelines about the prescribed structure of academic assignments (Cotton et al., 2023 ; Gardner & Giordano, 2023 ). This practice can facilitate the production of more cohesive assignments. In addition, teachers can use rubrics to assess assignments and blend automated and manual assessment methodologies to evaluate students’ comprehension of the subject matter (Cotton et al., 2023 ; Shoufan, 2023 ).

In summary, using ChatGPT is recommended for enhancing creativity, refining writing proficiency, and improving research abilities. Nonetheless, it is crucial to emphasise that ChatGPT should not be employed as a substitute for critical thinking and producing original work. While it serves as a valuable tool for augmentation, upholding the integrity of independent thought and authentic content creation in academic endeavours is essential.

4.2 Limitations

The present study acknowledges several limitations. Firstly, the reliance on Scopus as the primary data source for bibliometric analysis may have limitations in capturing the full landscape of relevant literature. Future research may consider incorporating additional databases like Web of Science to ensure a comprehensive assessment. Secondly, due to the English language restriction in the review, potentially relevant studies may have been omitted. Future research could enhance inclusivity by extending its scope to encompass papers written in languages other than English. Thirdly, the current study exclusively focused on journal articles. Expanding the scope to include diverse sources, such as conference proceedings or book chapters, could offer a more comprehensive overview.

Additionally, as a rapidly evolving field, literature published after our inclusion dates need capturing, and future studies should consider adjusting their inclusion criteria to accommodate the dynamic nature of the subject matter. Lastly, the specificity of the bibliometric data search, centred around terms like ChatGPT, AI, higher education, and academic integrity, may have excluded certain relevant articles. Future studies should consider employing more generalised search parameters to encompass synonyms associated with these terms.

4.3 Future scope

The findings of the study suggest new avenues for future research. The effectiveness of evaluation criteria for assessments incorporating ChatGPT-generated text needs to be investigated. Specifically, the appropriate level of ChatGPT-produced text that students may use in academic tasks or assessments has not been established. Research on the ethical implications of using AI tools such as ChatGPT in higher education is also needed. Issues pertaining to data confidentiality, bias, and transparency in algorithms used for decision-making remain to be addressed. Feasible approaches for mitigating the excessive reliance of scholars and learners on ChatGPT or similar AI models are needed. Researchers could also explore the implementation of verification processes that go beyond traditional plagiarism detection methods, accounting for the unique challenges posed by AI systems. Future research in this domain could focus on establishing guidelines and best practices for the integration of AI tools like ChatGPT in academic settings, ensuring a balance between technological innovation and the preservation of academic rigour. Finally, the literature on ChatGPT in higher education has largely focused on the medical and tourism sectors. Future researchers must explore applications of ChatGPT in other disciplines.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Adetayo, A. J. (2023). ChatGPT and librarians for reference consultations. Internet Reference Services Quarterly , 27 (3), 131–147.

Article   Google Scholar  

AlAfnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology , 3 (2), 60–68. https://doi.org/10.37965/jait.2023.0184 .

Alnaqbi, N. M., & Fouda, W. (2023). Exploring the role of ChatGPT and social media in enhancing student evaluation of teaching style in higher education using Neutrosophic sets. International Journal of Neutrosophic Science , 20 (4), 181–190.

Angelis, L. D., Baglivo, F., Arzilli, G., Privitera, G. P., Ferragina, P., Tozzi, A. E., & Rizzo, C. (2023). ChatGPT and the rise of large language models: The new AI-driven infodemic threat in public health. Frontiers in Public Health , 11 , 1–8.

Archambault, E., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing bibliometric statistics obtained from the web of Science and Scopus. Journal of the American Society for Information Science and Technology , 60 (7), 1320–1326.

Bajaj, Y., Kashiramka, S., & Singh, S. (2020). Application of capital structure theories: A systematic review. Journal of Advances in Management Research , 18 (2), 173–199. https://doi.org/10.1108/JAMR-01-2020-001 .

Bauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M., Fischer, M. R., & Fischer, F. (2023). Using natural language processing to support peer-feedback in the age of artificial intelligence: A cross-disciplinary framework and a research agenda. British Journal of Educational Technology , 54 (5), 1222–1245.

Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology , 61 (12), 2389–2404.

Cingillioglu, I. (2023). Detecting AI-generated essays: The ChatGPT challenge. International Journal of Information and Learning Technology , 40 (3), 259–268. https://doi.org/10.1108/IJILT-03-2023-0043 .

Clark, T. M. (2023). Investigating the Use of an Artificial Intelligence Chatbot with General Chemistry exam questions. Journal of Chemical Education , 100 (5), 1905–1916.

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International , 1–13. https://doi.org/10.1080/14703297.2023.2190148 .

Crawford, J., Cowling, M., & Allen, K. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice , 20 (3), 1–21. https://doi.org/10.53761/1.20.3.02 .

Crompton, H., & Burke, D. (2023). Artificial intelligence and higher education: The state of the field. International Journal of Educational Technology in Higher Education , 20 , 1–22. https://doi.org/10.1186/s41239-023-00392-8 .

Dehouche, N. (2021). Plagiarism in the age of massive generative pre-trained transformers (GPT-3). Ethics in Science and Environmental Politics , 2 , 17–23. https://doi.org/10.3354/esep00195 .

Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review , 11 (1), 1–16. https://doi.org/10.14763/2022.1.1615 .

Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research : A bibliometric analysis. Journal of Business Research , 109 , 1–14. https://doi.org/10.1016/j.jbusres.2019.10.039 .

Emenike, M., & Emenike, B. (2023). Was this title generated by ChatGPT? Considerations for artificial intelligence text-generation software programs for chemists and chemistry educators. Journal of Chemical Education , 100 (4), 1413–1418. https://doi.org/10.1021/acs.jchemed.3c00063 .

Eysenbach, G. (2023). The role of ChatGPT, generative language models, and artificial intelligence in medical education: A conversation with ChatGPT and a call for papers. JMIR Medical Education , 6 (9), 1–13. https://doi.org/10.2196/46885 .

Farrokhnia, M., Banihashem, K. S., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International , 1–15. https://doi.org/10.1080/14703297.2023.2195846 .

Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating academic answers generated using ChatGPT. Journal of Chemical Education , 100 (4), 1672–1675. https://doi.org/10.1021/acs.jchemed.3c00087 .

Firat, M. (2023). How chat GPT can transform autodidactic experiences and open education. Department of Distance Education, Open Education Faculty , Anadolu University, 1–6. https://doi.org/10.31219/osf.io/9ge8m .

Gardner, D. E., & Giordano, A. E. (2023). The challenges and value of undergraduate oral exams in the physical chemistry classrooms: A useful tool in the assessment toolbox. Journal of Chemical Education , 100 (5), 1705–1709.

Geerling, W., Mateer, G. D., Wooten, J., & Damodaran, N. (2023). ChatGPT has aced the test of understanding in college economics: Now what? The American Economist , 68 (2), 233–245.

Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education , 9 , 1–9. https://doi.org/10.2196/45312 .

Gisev, N., Bell, J. S., & Chen, T. F. (2013). Interrater agreement and interrater reliability: Key concepts, approaches, and applications. Research in Social and Administrative Pharmacy , 9 (3), 330–338.

Gupta, R., Pandey, R., & Sebastian, V. J. (2021). International entrepreneurial orientation (IEO): A bibliometric overview of scholarly research. Journal of Business Research , 125 , 74–88. https://doi.org/10.1016/j.jbusres.2020.12.00 .

Ibrahim, H., Asim, R., & Zaffar, F. (2023). Rethinking homework in the age of artificial intelligence. Intelligent Systems , 38 (2), 24–27.

Google Scholar  

Iskender, A. (2023). Holy or Unholy? Interview with Open AI’s ChatGPT. European Journal of Tourism Research , 34 , 3414. https://doi.org/10.54055/ejtr.v34i.3169 .

Ivanov, S., & Soliman, M. (2023). Game of algorithms: ChatGPT implications for the future of tourism education and research. Journal of Tourism Futures , 9 (2), 214–221.

Jarneving, B. (2007). Bibliographic coupling and its application to research-front and other core documents. Journal of Informetrics , 1 (4), 287–307.

Jeon, J., Lee, S., & Cho, S. (2023). A systematic review of research on speech-recognition chatbots for language learning: Implications for future directions in the era of large language models. Interactive Learning Environment , 1–19. https://doi.org/10.1080/10494820.2023.2204343 .

Johinke, R., Cummings, R., & Di Lauro, F. (2023). Reclaiming the technology of higher education for teaching digital writing in a post-pandemic world. Journal of University Teaching and Learning Practice , 20 (2), 1–18. https://doi.org/10.53761/1.20.02.01 .

Karaali, G. (2023). Artificial Intelligence, Basic skills, and quantitative literacy. Numeracy , 16 (1), 1–22. https://doi.org/10.5038/1936-4660.16.1.1438 .

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences , 103 (2023), 102274–102271. https://doi.org/10.1016/j.lindif.2023.102274 .

Lawrie, G. (2023). Establishing a delicate balance in the relationship between artificial intelligence and authenticate assessment in student learning. Chemistry Education Research and Practice , 24 (2), 392–393.

Lee, H. (2023). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Science Education , 1–6. https://doi.org/10.1002/ase.2270 .

Li, Y., Sha, L., Yan, L., Lin, J., Raković, M., Galbraith, K., Lyons, K., Gašević, D., & Chen, G. (2023). Can large language models write reflectively. Computer and Education: Artificial Intelligence , 4 (2023), 100140–100141.

Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. Lancet Digital Health , 5 (3), e105–106. https://doi.org/10.1016/S2589-7500(23)00019-5 .

Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education , 21 (2), 1–13.

Lin, C. C., Huang, A. Y. Q., Stephen, J. H., & Yang (2023). A review of AI-Driven conversational chatbots implementation methodologies and challenges (1999–2022). Sustainability , 15 (5), 4012. https://doi.org/10.3390/su15054012 .

Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials and limitations. Computers in Human Behavior: Artificial Humans , 100022 , 1–11.

Mishra, D., Gunasekaran, A., Papadopoulos, T., & Hazen, B. (2017). Green supply chain performance measures: A review and bibliometric analysis. Sustainable Production and Consumption , 10 , 85–99. https://doi.org/10.1016/j.spc.2017.01.003

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine , 151 (4), 264–269.

Nautiyal, R., Albrecht, J. N., & Nautiyal, A. (2023). ChatGPT and tourism academia. Annals of Tourism Research , 99 , 1–3.

Neumann, M., Rauschenberger, M., & Schon, E-M. (2023). 2023 IEEE/ACM 5th International Workshop on Software Engineering Education for the Next Generation (SEENG), Melbourne, Australia, 2023, pp. 29–32, https://doi.org/10.1109/SEENG59157.2023.00010 .

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ , 372 (71), 1–9. https://doi.org/10.1136/bmj.n71

Palomo, J., Figueroa-Domecq, C., & Laguna, P. (2017). Women, peace and security state-of-art: A bibliometric analysis in social sciences based on SCOPUS database. Scientometrics , 113 (1), 123–148. https://doi.org/10.1007/s11192-017-2484-x .

Patrício, L. D., & Ferreira, J. J. (2020). Blockchain security research: Theorizing through bibliographic-coupling analysis. Journal of Advances in Management Research , 18 (1), 1–35. https://doi.org/10.1108/JAMR-04-2020-0051 .

Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies . https://doi.org/10.1111/ijcs.12695 .

Pechenkina, K. (2023). Artificial intelligence for good? Challenges and possibilities of AI in higher education from a data justice perspective. In L. Czerniewicz, & C. Cronin (Eds.), Higher education for good: Teaching and learning futures (#HE4Good) . Open Book.

Perkins, M. (2023). Academic integrity considerations of AI large Language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice , 20 (2), 1–26. https://doi.org/10.53761/1.20.02.07 .

Rahman, M., Mostafizer, & Yutaka Watanobe. (2023). ChatGPT for Education and Research: Opportunities, threats, and strategies. Applied Sciences , 13 (9), 1–21. https://doi.org/10.3390/app13095783 .

Rana, S. (2023). AI and GPT for Management scholars and practitioners: Guidelines and implications. FIIB Business Review , 12 (1), 7–9.

Seetharaman, R. (2023). Revolutionising medical education: Can ChatGPT boost subjective learning and expression? Journal of Medical System , 47 (61), 1–4. https://doi.org/10.1007/s10916-023-01957-w .

Seth, I., Bulloch, G., & Lee, C. H. A. (2023). Redefining academic integrity, authorship, and innovation: The impact of ChatGPT on surgical research. Annals of Surgical Oncology , 30 , 5284–5285. https://doi.org/10.1245/s10434-023-13642-w .

Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE Access , 11, 38805–38818, 2023, https://doi.org/10.1109/ACCESS.2023.3268224 .

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of web of Science, Scopus and dimensions: A comparative analysis. Scientometrics , 126 , 5113–5142.

Skavronskaya, L., Hadinejad, A., & Cotterell, D. (2023). Reversing the threat of artificial intelligence to opportunity: A discussion of ChatGPT in tourism education. Journal of Teaching in Travel & Tourism , 23 , 1–6. https://doi.org/10.1080/15313220.2023.2196658 .

Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments , 1–15. https://doi.org/10.1080/10494820.2023.2209881 .

Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education , 6 (3), 355–366.

Subramani, M., Jallel, I., & Mohan, S. K. (2023). Evaluating the performance of ChatGPT in medical physiology university examination of phase I MBBS. Advances in Physical Education , 47 , 270–271.

Sun, G. H., & Hoelscher, S. H. (2023). The ChatGPT Storm and what Faculty can do. Nurse Educator , 48 , 119–124.

Tang, G. (2023a). Academic journals cannot simply require authors to declare that they used ChatGPT. Irish Journal of Medical Science , 192 (6), 3195–3196.

Tang, G. (2023b). Letter to editor: Academic journals should clarify the proportion of NLP-generated content in papers. Accountability in Research , 1–3. https://doi.org/10.1080/08989621.2023.2180359 .

Teixeira da Silva, J. A. (2023). How are authors’ contributions verified in the ICMJE model? Plant Cell Reports , 42 , 1529–1153. https://doi.org/10.1007/s00299-023-03022-9 .

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science , 379 (6630), 313–313.

Tlili, A., Shehata, B., Adarkwah, M., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments , 10 (15), 1–24.

Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics , 84 (2), 523–538.

Vijaya, V., & Mathur, H. P. (2023). A decade of donation-based crowdfunding: A bibliometric analysis using the SCOPUS Database. Purushartha , 15 (2), 32–51.

Vogels, E. A. (2023). A majority of Americans have heard of ChatGPT, but few have tried it themselves, https://www.pewresearch.org/short-reads/2023/05/24/a-majority-of-americans-have-heard-of-chatgpt-but-few-have-tried-it-themselves/ .

Wu, R., & Yu, Z. (2023). Do AI Chatbots improve students learning outcome? Evidence from a meta analysis. British Journal of Educational Technology . https://doi.org/10.1111/bjet.13334 .

Wu, T. Y., He, S. Z., Liu, J. P., Sun, S. Q., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica , 10 (5), 1122–1136. https://doi.org/10.1109/JAS.2023.123618 .

Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies , 1–25. https://doi.org/10.1007/s10639-023-11742-4 .

Yeadon, W., Inyang, O-O., Mizouri, A., Peach, A., & Testrow, C. P. (May 2023). The death of the short-term physics essay in the coming AI revolution. Physics Education , 58 , 1–13.

Zhai, X. (2022). ChatGPT user experience: Implications for education. SSRN , 4312418 , 1–18.

Download references

Open Access funding enabled and organized by CAUL and its Member Institutions

Author information

Authors and affiliations.

University Business School, Maharaja Ranjit Singh Punjab Technical University, Punjab, India

Pritpal Singh Bhullar

Department of Financial Planning and Tax, School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne, Australia

Mahesh Joshi

School of Engineering and Technology, CML‑NET & CREATE Research Centres, Central Queensland University, Queensland, Australia

Ritesh Chugh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ritesh Chugh .

Ethics declarations

Conflict of interest.

No Conflict of Interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Bhullar, P.S., Joshi, M. & Chugh, R. ChatGPT in higher education - a synthesis of the literature and a future research agenda. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12723-x

Download citation

Received : 04 October 2023

Accepted : 16 April 2024

Published : 02 May 2024

DOI : https://doi.org/10.1007/s10639-024-12723-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial Intelligence
• Generative AI
• Higher education
• Academic integrity
• Systematic review
• Bibliometric analysis
• Find a journal
• Publish with us
• Track your research

• Starting a Business
• Growing a Business
• Small Business Guide
• Business News
• Science & Technology
• Money & Finance
• For Subscribers
• Write for Entrepreneur
• Entrepreneur Store
• United States
• Asia Pacific
• Middle East
• South Africa

Copyright © 2024 Entrepreneur Media, LLC All rights reserved. Entrepreneur® and its related marks are registered trademarks of Entrepreneur Media LLC

I've Been Offered Every Job I've Interviewed For. Here Are 5 Questions I Ask Interviewers. Career consultant Kendal Lindstrom says these five questions are the winning formula for landing the role.

By Tim Paradis • May 9, 2024

Key Takeaways

• Kendal Lindstrom started a career-change consultancy after struggling to change jobs.
• She shared her strategy for acing job interviews, which includes having five key questions ready.
• They focus on areas such as company culture, team dynamics, and the employer's long-term plans.

This article originally appeared on Business Insider .

This as-told-to essay is based on a conversation with Kendal Lindstrom, 25, who lives in Scottsdale, Arizona. She runs a career-change consulting firm named Doux and works in tech. She recently posted a TikTok about five questions she has ready for a job interview. Lindstrom says she believes asking at least some of these questions is why she's always landed a role she interviewed for. The following has been edited for brevity and clarity.

I started Doux because I never liked to be put in a box in terms of my career. Coming out of college, I thought, "I just want to be known as the girl in fashion." I was so wrong. But I didn't know how to pivot into a new industry . It took me two years of connecting, trying, and failing. I found the framework of what Doux is now by failing.

After working in fashion, I got myself into medical sales. I then switched to tech because that's where my passions lie. It took me two years to go from fashion to medical sales. But from the day I decided I wanted to be a tech consultant, it only took me three weeks to get my offer letter.

The difference was I knew how to write my résumé. I knew how to become the candidate that they needed.

My formula is to map your résumé to the career you're going to, not the career you've been in. To get to my current job, I created a résumé that was unstoppable.

Usually, I tell my clients to reach out to the hiring manager. In this case, the hiring manager got to me within minutes of me submitting my résumé. The interview process was extensive, but, like I always tell my clients, it's about follow-ups.

I followed up three times because they had great candidates. But I needed to stay in front, and I needed to be the person they chose.

I had the drive

It's funny when I look back and talk to the executives who hired me. They're like: "You had no business being in tech. You had nothing on your résumé that told us that you would do a good job in this. But the way you presented yourself, it was a no-brainer to hire you because we knew you would get it." So, it's often more how you're presenting yourself in a professional realm rather than what you're saying to answer the questions.

I had drive, and that's what they were looking for. They were looking for someone young to grow with the company. If they wanted someone young, they weren't going to get all the experience in the software that they needed. But I was eager to learn, and however many hours outside work that took, I was willing to do it. I really drove home that it doesn't stop at 5 p.m. My job stops when my job is done.

Each day after work, I spent 30 minutes reading a training book my company had given me. Then, I tried to apply the knowledge for 30 minutes. The next day, I would get time on my boss's calendar and say: "This is what I learned yesterday. Tell me how you have seen this applied in scenarios with a client."

It took me about a year to really digest everything. It was tough, but it came down to whether I was willing to ask questions when I needed help rather than having too much pride and not asking anyone.

I've done a lot of interviews for my age because I kept my options open no matter where I was in my career. I've never wanted to be stagnant. So I have done upwards of 10 or 11 interviews, and I've never been told no because my goal was to make an employer feel like I had their best interests at heart and I wanted to be part of their company, which meant I needed to sell myself as a solution. And it's more about the questions you ask than the answers you get.

I have pretty thick skin

When I worked in medical sales — or even with some of the comments on my TikTok — so much was about my image. I was like, "What does my blonde hair have to do with the knowledge that I have?" Not that it ever hurt my feelings because I have pretty thick skin. In any industry, there will be people who would want to discredit someone's abilities because of how they look. But at the end of the day, I can use my brain to where people are like, "We need to listen to you."

@kendallindstrom it's more about the questions you ask than the answers you get. people want to talk about themselves. #interviewquestions #jobinterview #resume #careerchange #womeninbusines ♬ original sound - DOUX | CAREER CHANGE MGMT

Some of the comments on my TikTok have been so far off the mark. At the time of my interviews for my current job, I didn't have a website, and my social media wasn't publicly available. So, I got the job because of the things I said and the questions I asked, and not because of my appearance.

These are my five key questions:

What's the company culture like?

The first thing I tell people to ask is about company culture. That's a big one. It's such a make-it-or-break-it for enjoying your job. I wanted my audience to know that asking about it is so important because if you're miserable in your job, you're only setting yourself up to fail.

What's the lowdown on my predecessor?

The second one is, "What did the person who held this role before me do that was appreciated but not required based on the job description?" I suggest this one because I want my audience to put themselves in the role already. It's an assumptive selling tactic. I always say go into the interview and sell yourself.

I asked that question one time — "What are you going to miss most about this person?" — and the interviewer said, "Oh, they got Starbucks all the time." And I was like, "Great, I guess we'll be getting Starbucks for the office all the time."

What do my colleagues require?

The third question was, "How can I best suit the needs of my direct counterparts?" That came from wanting to understand — in the most professional way — the team you're walking into. It helps me understand and identify how I would fit into the team.

I've seen teams before where they just don't get along. But you don't know that until you sit down on the first day. And at that point, it's already too late. You're either leaving, or you've got to deal with this until you can figure out another job.

How successful is the team?

No. 4 is what the current state of the department is in reference to the bottom line. That has to do with asking about sales, of course, but I'm also asking: "Am I walking into a failing department? Are you expecting me to turn things around? Are you expecting me to just take the blame for something that's already failing? Or are you guys seeing numbers you've never seen before and need more people?" And, if so, "What did you do to see those numbers?"

What does the company's future look like?

My fifth question is my favorite. It's, "What's the company's three-year, five-year, and 10-year plan?" I love this one because I've never walked into a job and thought, "I'm only going to be here for one year," or "I'm only doing this to collect a paycheck." I always say, "Think like the CEO." I never want to go into a job and strive to just be an associate. That's just where you start.

All you really need — or maybe have time for — is one of these questions. So many people on my TikTok said, "That is too many questions. You're so high maintenance." I was like, "Just use one of them, and they'll be blown away." Because you're starting a whole other conversation that doesn't have to do with their questions for you. These are just concepts that I hope people can take with them as they go — little nuggets — to nail these interviews.

Want to be an Entrepreneur Leadership Network contributor? Apply now to join.

Editor's Pick Red Arrow

• This Mother and Daughter Were 'Kind of Fringe Weirdos' When They Started an Uncommon Business in Their Garage. Now They're in Major Retailers — and Victoria Beckham Is a Fan.
• Lock A Leadership Shortage Is Coming. Here's What Needs to Happen to Prevent It.
• Lock The Author of 'Million Dollar Weekend' Says This Is the Only Difference Between You and the Many 'Very, Very Dumb People' Making a Lot of Money
• What the NLRB Appeal of the Expanded Joint Employer Rule Judgment Means for Your Business
• Lock 12 Books That Self-Made Millionaires Swear By
• The Sweet Side Hustle She Started in an Old CVS Made $800,000 in One Year. Now She's Repeating the Success With Her Daughter — and They've Already Exceeded 8 Figures.

Most Popular Red Arrow

Get microsoft office plus windows 11 pro for $70 this week only.

Use Microsoft Office to streamline productivity and Windows 11 Pro for security, collaboration, and more.

63 Small Business Ideas to Start in 2024

We put together a list of the best, most profitable small business ideas for entrepreneurs to pursue in 2024.

Save 20% and Stay Loose with This Massager

Tension can be a distraction, which is bad for business.

How Small Businesses Can Master a Complex Labor Market

Navigating today's labor market is a high-stakes game for small businesses as they compete to attract top talent. Here are a few strategies for small businesses to consider as they build and strengthen their teams.

Drive Safe on Business Trips with This Car Display, Discounted to $90

Compatible with Apple CarPlay and Android Auto, this touchscreen display makes safer navigation easier.

My Startup Couldn't Raise VC Funding, So We Became Profitable. Here's How We Did It — And How You Can Too.

Four months ago, my startup reached profitability for the first time. It came after more than a year of active work and planning, and here's what it took.

Successfully copied link