Next Article in Journal
Research on Power Cable Intrusion Identification Using a GRT-Transformer-Based Distributed Acoustic Sensing (DAS) System
Previous Article in Journal
DFPoLD: A Hard Disk Failure Prediction on Low-Quality Datasets
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Informatics
Volume 12
Issue 3
10.3390/informatics12030074
informatics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Comprehensive Review of ChatGPT in Teaching and Learning Within Higher Education

by
Samkelisiwe Purity Phokoye
*,
Siphokazi Dlamini
,
Peggy Pinky Mthalane
,
Mthokozisi Luthuli
and
Smangele Pretty Moyane
Information and Corporate Management, Durban University of Technology, Durban 4000, South Africa
*
Author to whom correspondence should be addressed.
Informatics 2025, 12(3), 74; https://doi.org/10.3390/informatics12030074
Submission received: 11 April 2025 / Revised: 5 June 2025 / Accepted: 12 June 2025 / Published: 21 July 2025
Browse Figures
Review Reports Versions Notes

Abstract

Artificial intelligence (AI) has become an integral component of various sectors, including higher education. AI, particularly in the form of advanced chatbots like ChatGPT, is increasingly recognized as a valuable tool for engagement in higher education institutions (HEIs). This growing trend highlights the potential of AI to enhance student engagement and subsequently improve academic performance. Given this development, it is crucial for HEIs to delve deeper into the potential integration of AI-driven chatbots into educational practices. The aim of this study was to conduct a comprehensive review of the use of ChatGPT in teaching and learning within higher education. To offer a comprehensive viewpoint, it had two primary objectives: to identify the key factors influencing the adoption and acceptance of ChatGPT in higher education, and to investigate the roles of institutional policies and support systems in the acceptance of ChatGPT in higher education. A bibliometric analysis methodology was employed in this study, and a PRISMA diagram was used to explain the papers included in the analysis. The findings reveal the increasing adoption of ChatGPT within the higher education sector while also identifying the challenges faced during its implementation, ranging from technical issues to educational adaptations. Moreover, this review provides guidelines for various stakeholders to effectively integrate ChatGPT into higher education.

1. Introduction

The advent of advanced natural language processing technologies particularly ChatGPT has significantly impacted various sectors of society, including higher education. ChatGPT, a sophisticated language model developed by OpenAI, has revolutionized the way in which information is processed, communicated, and understood [1,2]. For instance, in the healthcare sector, ChatGPT has enhanced patient interactions through efficient and accurate information dissemination and support [3,4]. In the domain of customer service, ChatGPT algorithms swiftly address queries, provide personalized responses, and improve overall user satisfaction [5,6]. Recognizing these advancements, higher education institutions are now exploring the transformative potential of ChatGPT to enrich the educational experience and enhance content delivery [7,8].
In recent years, the landscape of higher education has experienced notable changes due to rapid technological progressions, resulting in the embrace of innovative pedagogical approaches like online learning, personalized educational pathways, and improved access to academic materials [9,10]. One of the key advancements in this realm is the emergence of ChatGPT as a pivotal instrument, enabling individualized guidance, immediate feedback, and the establishment of interactive educational settings [11,12]. As highlighted by [13] along with [14], ChatGPT plays a vital role in supporting learners with diverse educational requirements by offering personalized support and alternative clarifications on intricate subjects, thereby catering to various learning approaches and speeds. Therefore, students have incorporated ChatGPT into their higher education as it provides them with customized assistance, fostering a stimulating and dynamic learning atmosphere that readies them for the forthcoming demands of the professional landscape [15,16]. This tool also caters for international students, as it provides instant language translation and cultural insights, enhancing their comprehension and assimilation within academic circles [17]. Moreover, the utilization of ChatGPT in crafting accessible educational resources guarantees that students with disabilities have equal prospects to engage and excel in their academic pursuits [18]. These advancements underscore the transformative potential of ChatGPT in democratizing education and nurturing an inclusive learning environment that empowers all students to realize their utmost capabilities. Given these advancements, higher education institutions must investigate the global reception and implementation of ChatGPT in educational contexts, thereby maximizing its capacity to revolutionize learning and instructional methodologies.
Research has shown that ChatGPT helps to create personalized learning experiences and also supports educators in many ways. The authors of [19] highlighted that using ChatGPT in higher education brings significant benefits to educators, improving teaching practices. While integrating ChatGPT into education brings both opportunities and challenges, it also plays a valuable role in tasks such as drafting and refining scholarly papers, creating lecture materials, and designing exam questions tailored to specific learning goals [20,21]. Through the automation of these labor-intensive duties, educators can allocate more time to engaging with students and fostering innovations in education. This transition not only amplifies teaching effectiveness but also cultivates a more interactive and supportive learning atmosphere, ultimately enhancing educational achievements [22,23]. Additionally, the repercussions of ChatGPT in higher education hold significant implications for promoting inclusivity and accessibility. Hence, the aim of this research was to conduct a comprehensive review of ChatGPT in teaching and learning within higher education.

2. Literature Review

The literature indicates a growing interest in the utilization of ChatGPT as a supplementary educational tool to enrich education and enhance students’ motivation and academic performance [24]. ChatGPT has taken on a unique role in modern education, with many schools and universities—especially in developed countries—incorporating this technology into their teaching methods [25]. This pattern is consistent with the wider acceptance of technology in the educational domain, where a variety of technological tools and applications are being utilized to bolster and enhance learning experiences. As outlined in the EDUCAUSE 2024 Action Plan, AI technologies such as ChatGPT are progressively being employed to deliver customized learning experiences, streamline administrative duties, and provide immediate feedback to students, thereby enriching the overall educational journey [26]. Studies like [27] underscore that the incorporation of ChatGPT into educational settings significantly aids in the cultivation of students’ digital literacy skills, equipping them for a future driven by technology. This integration forms part of a broader movement towards integrating AI tools to facilitate interactive learning, furnish instantaneous feedback, and uphold personalized educational strategies [28].
Despite the prevalent utilization of ChatGPT among students and educators, numerous institutions of higher learning have not yet established comprehensive frameworks to oversee and regulate its application. This gap in governance has prompted concerns regarding academic integrity and the ethical application of artificial intelligence within educational domains [29,30]. Across various developing nations, the incorporation of AI technologies like ChatGPT into higher education is gaining momentum; however, the application of corresponding policies remains inconsistent. This inconsistency has led to possible misapplications, especially in environments where academic integrity and resource accessibility are already compromised [31,32]. Some universities have initiated the provision of resources and guidance concerning ethical usage; however, numerous institutions still lack explicit policies to govern the application of these technologies, potentially leading to issues such as plagiarism or the erosion of critical thinking skills [33,34]. In developing areas, the swift adoption of AI tools is advancing more rapidly than faculty training initiatives, leaving a substantial number of educators ill-equipped to employ these technologies effectively and ethically. This situation underscores the necessity for comprehensive faculty development programs to guarantee that AI tools are utilized in manners that enhance educational outcomes while upholding academic standards [34].
On the other hand, some studies have indicated that as much as the integration of AI technologies, especially ChatGPT, into educational environments holds great potential for transforming teaching and learning practices, higher education cannot fully rely on these technologies because of several limitations. For instance, Ref. [35] highlighted that the effectiveness of AI tools is significantly dependent on the quality of data they are trained on, which can introduce biases and inaccuracies if the data is flawed or unrepresentative. The authors of [36] argued that while AI technologies like ChatGPT offer significant advantages, they should be integrated with caution and always complemented by human expertise and oversight to ensure a balanced and effective educational approach. The literature further indicates that ChatGPT often lacks deep contextual understanding, leading to responses that may be contextually inappropriate or irrelevant. For instance, the model struggles with grasping the nuances of complex topics or the specific context of classroom discussions, which limits its ability to provide tailored educational support. This can result in students copying and pasting information or responses from the model without fully understanding the content, as highlighted by Ref. [37]. Supporting this, Ref. [38] suggested that students’ over-reliance on ChatGPT for learning support can diminish their critical thinking and problem-solving skills, as they rely on the tool for answers rather than engaging deeply with the material.
Building on these concerns, recent studies have explored the broader implications of integrating generative AI tools into higher education, identifying both promising opportunities and significant challenges. Research has shown that GenAI can enhance academic skill development and personalize learning experiences, contributing positively to student engagement and self-regulated learning [39,40]. However, ethical issues, academic integrity risks, and the need for clear institutional policies remain critical barriers to effective adoption. Additionally, students’ perceptions reveal a nuanced understanding of GenAI’s benefits and limitations, emphasizing the importance of designing implementations that address user needs while mitigating misuse [41]. Systematic reviews have also highlighted gaps in current research, calling for ongoing evaluation to optimize the use of GenAI in teaching and learning contexts [42]. These findings underscore the importance of balancing innovation with responsibility in the integration of GenAI within higher education.
Therefore, the continued development and integration of ChatGPT into educational settings should be balanced with human interactions to ensure a comprehensive education that combines the benefits of both technology and human involvement. As research and development in this area advances, it is essential to address ethical considerations and implement strategic plans to fully realize the potential of ChatGPT in education.

2.1. Factors Influencing the Adoption and Acceptance of ChatGPT in Higher Education

ChatGPT, being an integral component of intelligent educational ecosystems, possesses the capability to revolutionize pedagogical approaches, enrich educational prospects, and foster student autonomy and self-regulated learning. The integration and approval of ChatGPT within higher education encounter numerous obstacles. These include challenges related to resource allocation and costs for the implementation of AI technologies in educational settings [43], which may act as a deterrent for numerous academic institutions. The allocation of resources towards AI ventures in a way that detracts from other essential educational requirements is a concern raised in [44], which highlights that implementing AI in underfunded educational settings may divert limited resources from critical needs such as teacher training, infrastructure, and curriculum development. Additionally, [45] emphasized the importance of developing detailed resource allocation plans to ensure that investments in AI do not compromise other vital educational expenditures. Apprehensions regarding plagiarism and academic dishonesty were reiterated in [46], citing instances where students inappropriately utilize AI tools to complete assignments, along with the complexities of identifying AI-generated content. While the advantages of ChatGPT for students are evident, ethical considerations, such as academic integrity and AI bias, prompt inquiries into the impartiality and precision of AI-generated materials. The authors of Ref. [47] pointed out the limitations of this technology in furnishing precise and contextually suitable information, underscoring the dangers of misinformation.
Privacy and data security concerns further complicate the assimilation of ChatGPT, with potential risks of data breaches and breaches in confidentiality. The European Union Agency for Cybersecurity [48,49] cautions against vulnerabilities in AI systems that could result in substantial data breaches. The educational challenges encompass apprehensions of excessive dependence on AI, potentially diminishing critical thinking abilities. Further impediments to the widespread acceptance of ChatGPT in educational institutions include technological limitations, resistance to change among educators and students, high implementation expenses, challenges in accessibility, regulatory obstacles, and issues of cultural trust. Supporting the aforementioned points, Ref. [50] contended that an overreliance on AI tools may undermine the cultivation of critical thinking and problem-solving skills in students. Conversely, the author of Ref. [51] expressed doubts regarding the quality and suitability of AI-generated educational content, questioning its alignment with educational standards.
The absence of regulatory frameworks and guidelines for AI utilization in education was emphasized in [51]. Policy constraints at the institutional and governmental levels can also obstruct the adoption of AI tools, as noted in [52]. A recurring theme in the literature is the lack of confidence in AI systems among educators and students, with scholars [53] deliberating on the cultural and social barriers to AI integration in education. Additionally, the technology presents accessibility challenges, with [54] highlighting the digital divide and underscoring the disparities in technology access and internet connectivity that may impede AI tool adoption in education. Furthermore, the usability of AI tools not catering to all students, including those with disabilities, is a significant concern, as highlighted by Seale [55].

2.2. Institutional Support for the Integration of AI Tools (ChatGPT) in Higher Education

The integration of artificial intelligence (AI) tools in higher education has the potential to significantly enhance teaching and learning experiences. However, the successful adoption of these technologies relies heavily on the support and policies established by educational institutions. Effective institutional policies and robust support systems are essential to address ethical considerations, ensure equitable access, and provide necessary training for faculty, staff, and students [56]. Institutional support for the integration of AI tools, such as ChatGPT, in higher education is increasingly recognized as essential for enhancing teaching and learning experiences. Prior research has indicated that effective institutional frameworks can facilitate the adoption of AI technologies by providing necessary resources, training, and policy guidelines to educators and students alike [57]. For instance, Ref. [58] highlighted that institutions that actively promote AI literacy and provide technical support see higher engagement levels among faculty and students. Moreover, Ref. [59] emphasized the importance of collaborative efforts between educational institutions and technology providers to ensure that AI tools are effectively tailored to meet educational needs.
However, the authors of Ref. [60] cautioned that while institutional support is crucial, there are challenges related to the digital divide and varying levels of readiness among institutions, which can hinder the equitable integration of AI tools. The authors of [61] further stressed the need for ongoing evaluation and adaptation of institutional strategies to keep pace with rapid technological advancements. Thus, a multifaceted approach is necessary for successful AI integration in higher education. On the other hand, faculty and staff require access to educational programs that provide them with the necessary skills to proficiently utilize AI tools like ChatGPT in their instructional and administrative responsibilities. The provision of materials such as tutorials, workshops, and technical assistance can facilitate the seamless integration of these tools into the academic curriculum and teaching methodologies [62]. It is imperative for educational institutions to invest in the required technological framework to facilitate the implementation of AI tools, which encompasses guaranteeing sufficient internet bandwidth, modern hardware, and software licenses. Continuous technical support plays a crucial role in resolving any arising issues and aiding users in optimizing the functionality of AI tools.
Fostering a culture that promotes experimentation and innovation can enable educators to explore novel approaches to incorporating AI into their teaching methodologies. Educational institutions can endorse pilot initiatives and allocate resources for research on the effects of AI tools on educational outcomes. Therefore, the synchronization of AI policies with the mission and values of the institution is imperative in securing support from stakeholders and ensuring that AI endeavors align with broader educational goals, as highlighted in [63]. Additionally, according to [64], there is an emphasis on the importance of comprehensive ethical standards to regulate the utilization of AI, addressing issues such as privacy, biases, and ethical usage. This backing guarantees that faculty and staff are equipped to effectively utilize AI tools, supported by strong technological infrastructure and continual feedback mechanisms [65].

2.3. Ethical Issues Arising with the Adoption of ChatGPT in Teaching and Learning

As AI becomes increasingly prominent in the realm of education, paramount concerns about privacy and data security take center stage. The adoption of ChatGPT in education brings forth numerous ethical considerations that need careful examination to ensure responsible and equitable use [66]. AI systems, including ChatGPT, inadvertently perpetuate and amplify the existing biases present in their training data [67]. These biases lead to unfair treatment of certain groups of students, potentially reinforcing stereotypes and discrimination [68]. Students from underprivileged backgrounds may lack access to the necessary technological infrastructure, exacerbating the digital divide [69]. Ensuring equitable access to AI tools is crucial to prevent further marginalization of disadvantaged students. The use of AI in education involves the collection and processing of vast amounts of student data. Ensuring the privacy and security of this data is paramount, as breaches can have serious implications for students and institutions [70]. The integration of AI into educational practices can alter the traditional roles of teachers. While AI can assist with administrative tasks and provide personalized learning experiences, it should not replace the critical role of educators in providing mentorship, emotional support, and human interaction [71]. Balancing AI assistance with traditional teaching methods is crucial to maintaining the integrity of the educational process. Ensuring the ethical use of ChatGPT involves establishing guidelines for appropriate usage and preventing misuse. Educators and students need to be aware of the ethical implications of using AI and adhere to best practices to avoid potential harms [72].

3. Methodology

This section delineates the methodologies utilized for acquiring published articles concerning AI technology and tools in the realm of education, with a specific emphasis on ChatGPT (Version 1.6.20) as a writing aid. In this research, a bibliometric analysis was conducted, guided by a PRISMA diagram to explain how the papers were included for analysis. The study commenced by pinpointing and obtaining studies from the literature on the utilization of AI technologies in academic establishments for scholarly and scientific writing. Prominent databases such as Scopus, Science Direct, and Web of Science underwent a systematic review in three distinct phases: identification, screening, and inclusion. These databases were chosen due to their extensive coverage of scientific and scholarly publications spanning diverse fields, including technology, computer science, artificial intelligence, and education. Keywords were meticulously formulated to correlate with the subject matter and research inquiries pertaining to AI in education, the application of ChatGPT in higher education, and the future of writing with AI within educational contexts. The review included studies published between 2020 and 2025. This time range was selected to ensure comprehensive coverage of both foundational and recent developments related to AI in education, particularly the application of ChatGPT in higher education. Furthermore, Boolean search operators (‘AND’, ‘OR’, and ‘NOT’) were utilized to enhance the precision of the search outcomes.
The criteria for inclusion were as follows: (I) published peer-reviewed articles concentrating on the influence of AI technologies (ChatGPT) in higher education institutions; (II) articles published in the English language; and (III) conference papers. These criteria guaranteed that the chosen articles were relevant and in harmony with the aims of the study. Exclusions were enforced on articles that failed to meet these criteria, such as book chapters and editorial notes. The PRISMA shown in Figure 1 illustrates the number of papers and records retrieved using the identified keywords during the search. Initially, the search yielded a total of 3874 articles were obtained and retrieved from the three databases. To streamline the procedure and ensure the caliber and pertinence of this study, 281 duplicate documents were eliminated prior to screening, followed by the removal of 1741 papers that were irrelevant to the subject and the specified keywords of the articles. The documents evaluated for eligibility and encompassed in the review totaled 1852.

4. Results

In total, 3874 papers were collected within all fields. A first quick content check was conducted by only selecting titles, abstracts, and keywords to determine whether the content of the articles aligned with the criteria mentioned above. After the papers that did not meet the criteria were excluded, a total of 1852 papers were considered. The VOS viewer model was used to analyze the data. A VOS viewer (Version 1.6.20) is a software tool used for creating and visualizing bibliometric networks; it offers advanced text mining capabilities to construct co-occurrence networks of significant terms from the scientific literature, aiding in the analysis of research trends and relationships [73].
According to Figure 2 below the co-occurrence of keywords in publications related to ChatGPT highlights the frequency of key terms and the relationships among them. This visualization not only emphasizes the central position of ChatGPT within the broader field of artificial intelligence research but also highlights the interconnections of various associated terms, such as "large language model" and "natural language processing." As these keywords form clusters, they unveil developing research trends that could impact future technological and educational advancements. For example, exploring how educators can utilize tools like ChatGPT to improve learning outcomes might be a significant area of study, particularly due to its potential for customizing educational experiences. Moreover, the results of this research indicate a growing interest among professionals in library and information science regarding the effects of AI on information retrieval systems, signaling a movement towards incorporating advanced technologies into conventional practices. The diagram offers a thorough summary of research related to ChatGPT, highlighting its potential in education and healthcare, technological elements, and ethical considerations. This comprehension can direct future research efforts towards aligning the development of ChatGPT with educational requirements, ethical principles, and user satisfaction.
At the core lies “ChatGPT”, encircled by various clusters. The green cluster concentrates on higher education and learning, featuring terms like “higher education”, “university students”, and “assessment”, suggesting substantial research on the integration of ChatGPT into educational environments. The red cluster pertains to human research, encompassing “human”, “medical research”, and “mental health”, indicating an interest in applying ChatGPT in the healthcare and medical domains. The blue cluster centers on natural language processing and associated technologies, incorporating terms such as “natural language processing”, “machine learning”, and “deep learning.” The purple cluster consists of terms related to user satisfaction and perception, such as “student satisfaction”, “perception”, and “trust”, indicating interest in how users perceive and experience satisfaction with ChatGPT. The smaller yellow cluster encompasses specific technologies and applications like “chatbots” and “AI literacy”.
The prominent themes encompass education and learning, with investigations evaluating the impact of ChatGPT on teaching methodologies and student involvement. There is also notable interest in its application in medical research, along with the technological foundations of ChatGPT, including progressions in machine learning and AI morality. The emerging patterns underscore concerns regarding AI ethics, transparency, and understanding user viewpoints.
Figure 3 below showcases the top five countries with the most publications on ChatGPT from 2022 to 2024. Australia leads with 387 publications, closely followed by Malaysia with 385, indicating significant interest and research activity in these regions. Singapore, Japan, and Romania also show substantial contributions, with 336, 215, and 210 publications, respectively. This distribution suggests that the Asia–Pacific region is highly active in AI research, particularly in exploring the applications and implications of ChatGPT in higher education. The close numbers for Australia and Malaysia imply competitive research output, potentially driven by strong academic and technological frameworks supporting AI research in these countries. The prominent research activity in these countries can be attributed to several factors, including robust technological infrastructures, significant investments in AI and higher education, and a strong presence of academic institutions prioritizing AI research. This trend highlights global recognition of the importance of ChatGPT and similar AI technologies in transforming educational practices. This focus on AI research in the top contributing countries underscores their commitment to advancing AI-driven educational tools, addressing ethical considerations, and exploring practical applications to enhance learning experiences. Overall, the data reflects a growing global emphasis on integrating AI technologies like ChatGPT into educational frameworks, driven by competitive academic environments and supportive research ecosystems.
Figure 4 below shows that the number of publications in 2022 was relatively low. This is likely due to ChatGPT being a relatively new technology, with researchers just beginning to explore its applications and implications at that time. There was a notable increase in publications in 2023. This indicates growing interest and research activity around ChatGPT. The factors contributing to this surge could include increased adoption of ChatGPT in various educational and professional settings, as well as emerging discussions about the ethical, social, and practical implications of using AI in education. With more researchers recognizing the potential and challenges associated with ChatGPT, the trend continued with more publications in early 2024. This trend implies sustained interest and persistent scholarly endeavors, likely fueled by ongoing enhancements and advancements in ChatGPT, heightened recognition and incorporation of ChatGPT within higher education and other sectors, and ongoing deliberations and contention on the ethical and practical facets of AI deployment.
Figure 5 below illustrates the top 10 research areas for ChatGPT publications, highlighting the diverse fields where ChatGPT’s impact is being explored. The leading area, Computers and Education: Artificial Intelligence, dominates with approximately 50 publications, reflecting the significant interest in leveraging AI for educational purposes. This suggests that researchers are keenly focused on understanding and enhancing how AI technologies like ChatGPT can revolutionize teaching and learning processes, improve educational outcomes, and integrate seamlessly into educational systems. Following closely are Heliyon and Education and Information Technologies, with around 35 and 30 publications, respectively. These areas indicate a strong emphasis on the practical applications and theoretical advancements in educational technologies. The presence of conference proceedings, such as the ACM International Conference Proceeding Series, further underscores the active dissemination and discussion of ChatGPT research in academic and professional communities. Other notable areas include Education Sciences and Technology in Society, which highlight the broader societal implications and interdisciplinary approaches to integrating AI in education. The spread across various journals and conferences illustrates the widespread recognition of ChatGPT’s potential and the multifaceted research efforts to explore its capabilities and impacts.
According to Figure 6 below, the word cloud generated from the abstracts highlights the key themes and frequently discussed terms in the publications Commonly highlighted words include “students”, “education”, “AI”, “learning”, “technology”, and “performance”, reflecting the focus on educational applications and the impact of AI technologies. The analysis shows a robust and growing body of research on ChatGPT, driven by its increasing relevance in education and other fields. Ethical considerations, equitable access, and integration with traditional teaching methods are central themes in the research community. The leading countries in this research are making significant contributions to understanding and harnessing the potential of ChatGPT.

5. Discussion

This study conducted a thorough bibliometric analysis using the PRISMA diagram methodology to assess the development and implications of ChatGPT in higher education institutions (HEIs). This approach ensured a systematic review of the literature from leading databases like Scopus, Science Direct, and Web of Science, providing a comprehensive overview of the topic. The VOS viewer model was utilized to analyze the data and construct co-occurrence networks of significant terms from the scientific literature, providing insights into research trends and relationships within the field. The findings revealed a significant interest in the application of ChatGPT in education, particularly in enhancing teaching methodologies, student involvement, and academic writing assistance. The analysis also highlighted emerging concerns regarding AI ethics, transparency, and user perspectives, indicating the need for further research in these areas to address potential challenges and ensure responsible implementation. The literature review emphasized the growing utilization of ChatGPT as a supplementary educational tool to enrich learning experiences and improve academic performance. This trend aligns with the broader integration of technology in education, reflecting a shift towards innovative teaching practices and the adoption of AI-driven solutions in academic settings.
These capabilities not only support students in their academic pursuits, but also prepare them for future workforce challenges by developing their critical thinking and problem-solving skills. Based on the findings from the literature presented above, it can be said that cost and accessibility, resistance to change, technical limitations, dependence on technology, bias and ethical considerations, privacy and data security, and academic integrity and plagiarism concerns are the key factors influencing the adoption and acceptance of ChatGPT in higher education institutions (HEIs). These factors must be addressed through comprehensive strategies and policies to ensure the effective and ethical integration of AI tools like ChatGPT into higher education. Studies in the literature have identified several challenges in adopting ChatGPT, such as technical issues, data biases, and the need for human oversight. Institutions must develop policies that address ethical considerations, ensure equitable access, and provide training for educators and students. This includes aligning AI policies with institutional values, establishing ethical guidelines, and promoting professional development. Such measures are essential to maximizing the benefits of ChatGPT while mitigating risks and ensuring responsible and equitable AI use in education.
Furthermore, the findings revealed that the geographical distribution of research activity, with Australia, Malaysia, Singapore, Japan, and Romania leading in publications, reflects the global recognition of ChatGPT’s potential to transform educational practices. These countries’ robust technological infrastructures and significant investments in AI and higher education have fostered a conducive environment for AI research, emphasizing the importance of continued investment and support for AI-driven educational tools. In conclusion, while ChatGPT offers significant benefits for enhancing teaching and learning experiences in higher education, it is crucial to address the associated challenges and ethical considerations. By developing supportive institutional policies and maintaining a balance between AI and human interactions, higher education institutions can harness the full potential of ChatGPT to create an inclusive, dynamic, and effective learning environment.

6. Conclusion

The integration of ChatGPT in higher education presents a transformative opportunity to enhance learning experiences, streamline administrative tasks, and support personalized education plans. However, realizing these benefits requires robust institutional support, including the development of clear policies, the provision of adequate training and resources, and fostering an environment that encourages innovation and adaptation. By addressing ethical, pedagogical, and operational challenges, higher education institutions can effectively harness the potential of ChatGPT, ensuring that it contributes positively to educational outcomes and prepares students for the evolving demands of the modern world. This study’s insights contribute to the ongoing discourse on the role of AI in education, highlighting the significance of user perspectives, transparency, and ethical guidelines in shaping the future of AI-enhanced learning experiences.

Author Contributions

Data curation, Formal analysis, Investigation, Methodology, Software, Writing—review & editing, Writing—original draft: S.P.P.; Conceptualization, Funding acquisition, Project, Administration; Supervision: P.P.M.; Formal analysis, Investigation, Validation, Writing—review & editing: S.D. Data curation, Methodology, Visualization, Writing—review & editing: M.L. Project administration, Supervision, Writing—original draft: S.P.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Celik, I. Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Comput. Hum. Behav. 2023, 138, 107468. [Google Scholar] [CrossRef]
  2. Radford, A.; Kim, J.W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning, Online, 18–24 July 2021; pp. 8748–8763. [Google Scholar]
  3. Park, Y.; Doo, M.Y. Role of AI in blended learning: A systematic literature review. Int. Rev. Res. Open Distrib. Learn. 2024, 25, 164–196. [Google Scholar] [CrossRef]
  4. Abas, M.A.; Arumugam, S.E.; Yunus, M.M.; Rafiq, K.R.M. ChatGPT and personalized learning: Opportunities and challenges in higher education. Int. J. Acad. Res. Bus. Soc. Sci. 2023, 13, 3536–3545. [Google Scholar] [CrossRef] [PubMed]
  5. Dixit, S.; Maurya, M.; Jain, V.; Subramaniam, G. (Eds.) Artificial Intelligence-Enabled Businesses: How to Develop Strategies for Innovation; John Wiley & Sons: Hoboken, NJ, USA, 2024. [Google Scholar]
  6. Chen, L.; Chen, P.; Lin, Z. Artificial intelligence in education: A review. IEEE Access 2020, 8, 75264–75278. [Google Scholar] [CrossRef]
  7. Rodrigues, C.; Reis, A.; Pereira, R.; Martins, P.; Sousa, J.; Pinto, T. A review of conversational agents in education. In International Conference on Technology and Innovation in Learning, Teaching and Education, Lisbon, Portugal, 31 August–2 September 2022; Springer: Cham, Switzerland, 2022; pp. 461–467. [Google Scholar]
  8. Roll, I.; Wylie, R. Evolution and Revolution in Artificial Intelligence in Education. Int. J. Artif. Intell. Educ. 2016, 26, 582–599. [Google Scholar] [CrossRef]
  9. Johnson, N.; Veletsianos, G.; Seaman, J.U.S. faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 pandemic. Online Learn. 2020, 24, 6–21. [Google Scholar] [CrossRef]
  10. Martin, F.; Bolliger, D.U. Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learn. 2018, 22, 205–222. [Google Scholar] [CrossRef]
  11. Nguyen, A.; Kremantzis, M.; Essien, A.; Petrounias, I.; Hosseini, S. Enhancing student engagement through artificial intelligence (AI): Understanding the basics, opportunities, and challenges. J. Univ. Teach. Learn. Pract. 2024, 21, 1–13. [Google Scholar] [CrossRef]
  12. Kelly, A.; Sullivan, M.; Strampel, K. Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. J. Univ. Teach. Learn. Pract. 2023, 20, 12. [Google Scholar] [CrossRef]
  13. Clark, T.; Hernandez, J. AI-powered personalization in higher education: Addressing diverse learning needs with ChatGPT. J. Educ. Technol. Innov. 2023, 19, 102–117. [Google Scholar]
  14. Zirar, A. Exploring the impact of language models, such as ChatGPT, on student learning and assessment. Rev. Educ. 2023, 11, e3433. [Google Scholar] [CrossRef]
  15. Dempere, J.; Modugu, K.; Hesham, A.; Ramasamy, L.K. The impact of ChatGPT on higher education. In Frontiers in Education; Frontiers Media SA: Lausanne, Switzerland, 2023; Volume 8, p. 1206936. [Google Scholar]
  16. Woolf, B.P. Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning; Morgan Kaufmann: Burlington, MA, USA, 2010. [Google Scholar]
  17. Karataş, F.; Abedi, F.Y.; Gunyel, F.O.; Karadeniz, D.; Kuzgun, Y. Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners. Educ. Inf. Technol. 2024, 29, 19343–19366. [Google Scholar] [CrossRef]
  18. Sun, S.-P.; Liao, H.-T.; Wu, X. The role of big data and artificial intelligence in education and education research: A literature mapping. In Proceedings of the 6th International Conference on Humanities and Social Science Research (ICHSSR 2020), Hangzhou, China, 10–12 April 2020; Atlantis Press: Dordrecht, The Netherlands; pp. 241–245. [Google Scholar]
  19. Almogren, A.S.; Al-Rahmi, W.M.; Dahri, N.A. Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon 2024, 10, e31887. [Google Scholar] [CrossRef] [PubMed]
  20. Anderson, J. The Changing Role of Teachers in the Age of AI. Educ. Technol. Res. 2022, 34, 123–135. [Google Scholar]
  21. Halaweh, M. The educational potential of ChatGPT: Opportunities and challenges of using generative AI in higher education. Educ. Inf. Technol. 2023, 28, 12253–12271. [Google Scholar]
  22. Zawacki-Richter, O.; Marín, V.I.; Bond, M.; Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education—Where are the educators? Int. J. Educ. Technol. High. Educ. 2019, 16, 1–27. [Google Scholar] [CrossRef]
  23. Chiu, T.K. Future research recommendations for transforming higher education with generative AI. Comput. Educ. Artif. Intell. 2024, 6, 100197. [Google Scholar] [CrossRef]
  24. Bahrini, A.; Abbas, M.; Dwivedi, Y.K. Does ChatGPT enhance student learning? A systematic review and research agenda. Comput. Educ. 2024, 205, 104917. [Google Scholar]
  25. Chan, C.K.Y. A comprehensive AI policy education framework for university teaching and learning. Int. J. Educ. Technol. High. Educ. 2023, 20, 38. [Google Scholar] [CrossRef]
  26. Siemens, G.; Long, P. Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE. Rev. 2011, 46, 30–32. [Google Scholar]
  27. Popenici, S.A.D.; Kerr, S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn. 2017, 12, 22. [Google Scholar] [CrossRef] [PubMed]
  28. Zhu, G.; Fan, X.; Hou, C.; Zhong, T.; Seow, P.; Shen-Hsing, A.C.; Rajalingam, P.; Yew, L.K.; Poh, T.L. Embrace opportunities and face challenges: Using ChatGPT in undergraduate Students’ collaborative interdisciplinary learning. arXiv 2023, arXiv:2305.18616. [Google Scholar] [CrossRef]
  29. Strzelecki, A. Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innov. High. Educ. 2024, 49, 223–245. [Google Scholar] [CrossRef]
  30. Wang, S.; Wang, F.; Zhu, Z.; Wang, J.; Tran, T.; Du, Z. Artificial intelligence in education: A systematic literature review. Expert Syst. Appl. 2024, 252, 124167. [Google Scholar] [CrossRef]
  31. Evangelista, E.D.L. Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education. Contemp. Educ. Technol. 2025, 17, ep559. [Google Scholar] [CrossRef] [PubMed]
  32. Moya, B.; Eaton, S.E.; Pethrick, H.; Hayden, K.A.; Brennan, R.; Wiens, J.; McDermott, B.; Lesage, J. Academic integrity and artificial intelligence in higher education contexts: A rapid scoping review protocol. Can. Perspect. Acad. Integr. 2023, 5, 59–75. [Google Scholar] [CrossRef]
  33. Ghimire, A.; Edwards, J. From guidelines to governance: A study of ai policies in education. In Proceedings of the International Conference on Artificial Intelligence in Education, Recife, Brazil, 8–12 July 2024; Springer Nature Switzerland: Cham, Switzerland; pp. 299–307. [Google Scholar]
  34. Huang, J.; Wu, J.; Wang, Q.; Yuan, K.; Li, J.; Lu, D. From Prohibition to Adoption: How Hong Kong Universities Are Navigating ChatGPT in Academic Workflows. arXiv 2024, arXiv:2410.01695. [Google Scholar] [CrossRef]
  35. Lin, X.; Chan, R.Y.; Sharma, S.; Bista, K. The impact of Artificial Intelligence (AI) on global higher education: Opportunities and challenges of using ChatGPT and Generative AI. In ChatGPT and Global Higher Education: Using Artificial Intelligence in Teaching and Learning; STAR SCHOLARS: Baltimore, MD, USA, 2024; p. 1. [Google Scholar]
  36. Schwabe, D.; Becker, K.; Seyferth, M.; Klaß, A.; Schaeffter, T. The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review. NPJ Digit. Med. 2024, 7, 203. [Google Scholar] [CrossRef] [PubMed]
  37. Dreyfus, H.L. What Computers Still Can’t Do: A Critique of Artificial Reason; MIT Press: Boston, MA, USA, 1992. [Google Scholar]
  38. Henriksen, D.; Mishra, P.; The Deep-Play Research Group. We Teach Who We Are: Creativity, Technology, and Teacher Professional Identity. TechTrends 2021, 65, 371–380. [Google Scholar]
  39. Xia, Q.; Weng, X.; Ouyang, F.; Lin, T.J.; Chiu, T.K. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int. J. Educ. Technol. High. Educ. 2024, 21, 40. [Google Scholar] [CrossRef]
  40. McGrath, C.; Farazouli, A.; Cerratto-Pargman, T. Generative AI chatbots in higher education: A review of an emerging research area. High. Educ. 2025, 89, 1533–1549. [Google Scholar] [CrossRef]
  41. Chan, C.K.Y.; Hu, W. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. arXiv 2023, arXiv:2305.00290. [Google Scholar] [CrossRef]
  42. Ogunleye, B.; Zakariyyah, K.I.; Ajao, O.; Olayinka, O.; Sharma, H. A systematic review of generative AI for teaching and learning practice. arXiv 2024, arXiv:2406.09520. [Google Scholar] [CrossRef]
  43. Chukwuere, J.E.; Handoko, B.L. The future of generative AI chatbots in higher education. J. Emerg. Technol. 2024, 4, 36–44. [Google Scholar]
  44. Muranga, K.; Muse, I.S.; Köroğlu, E.N.; Yildirim, Y. Artificial Intelligence and Underfunded Education. Lond. J. Soc. Sci. 2023, 6, 56–68. [Google Scholar] [CrossRef]
  45. Southern Regional Education Board. Develop Resource Allocation Plans. 2025. Available online: https://www.sreb.org/ai-rec-6-resource-allocation-plans (accessed on 8 May 2025).
  46. Cotton, D.R.E.; Cotton, P.A.; Shipway, J.R. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int. 2023, 61, 228–239. [Google Scholar] [CrossRef]
  47. Marcus, G.; Davis, E. Rebooting AI: Building Artificial Intelligence We Can Trust; Pantheon Books: New York, NY, USA, 2019. [Google Scholar]
  48. ENISA. Artificial Intelligence Cybersecurity Challenges. European Union Agency for Cybersecurity. 2020. Available online: https://www.enisa.europa.eu/publications/artificial-intelligence-cybersecurity-challenges (accessed on 8 May 2025).
  49. Panc, D. An analysis of the European Union multifaceted approach to addressing the evolving cybersecurity challenges. In Proceedings of the Conferința Internațională Educație și Creativitate Pentru o Societate Bazată pe Cunoaștere-DREPT, 17(XVII), București, Romania, 22–23 November 2023; pp. 112–116. [Google Scholar]
  50. Selwyn, N. Should Robots Replace Teachers? AI and the Future of Education; Polity: Cambridge, UK, 2019. [Google Scholar]
  51. Williamson, B. Big Data in Education: The digital Future of Learning, Policy and Practice; Sage: New York, NY, USA, 2017. [Google Scholar]
  52. Williamson, B. The social life of AI in education. Int. J. Artif. Intell. Educ. 2024, 34, 97–104. [Google Scholar] [CrossRef]
  53. Aoun, J.E. Robot-Proof: Higher Education in the Age of Artificial Intelligence; MIT press: Cambridge, MA, USA, 2017. [Google Scholar]
  54. Warschauer, M.; Grimes, D. Audience, Authorship, and Artifact: The Emergent Semiotics of Web 2.0. Annu. Rev. Appl. Linguist. 2007, 27, 1–23. [Google Scholar] [CrossRef]
  55. Olawade, D.B.; Bolarinwa, O.A.; Adebisi, Y.A.; Shongwe, S. The role of artificial intelligence in enhancing healthcare for people with disabilities. Soc. Sci. Med. 2025, 364, 117560. [Google Scholar] [CrossRef] [PubMed]
  56. Bower, M.; Christensen, C.M. Disruptive technologies: Catching the wave. Harv. Bus. Rev. 1995, 73, 43–53. [Google Scholar]
  57. Spivakovsky, O.V.; Omelchuk, S.A.; Kobets, V.V.; Valko, N.V.; Malchykova, D.S. Institutional policies on artificial intelligence in university learning, teaching and research. Inf. Technol. Learn. Tools 2023, 97, 181. [Google Scholar] [CrossRef]
  58. Aristovnik, A.; Umek, L.; Brezovar, N.; Keržič, D.; Ravšelj, D. The role of ChatGPT in higher education: Some reflections from public administration students. In International Conference on Technology in Education, Hong Kong, China, 19–21 December 2023; Springer: Singapore, 2023; pp. 254–263. [Google Scholar]
  59. Puerta-Beldarrain, M.; Gomez-Carmona, O.; Sanchez-Corcuera, R.; Casado-Mansilla, D.; Lopez-de-Ipina, D.; Chen, L. A multifaceted vision of the Human-AI collaboration: A comprehensive review. IEEE Access. 2025. [CrossRef]
  60. Ahmed, F. The digital divide and AI in education: Addressing equity and accessibility. J. AI Integr. Educ. 2024, 1, 12–23. [Google Scholar]
  61. Korseberg, L.; Elken, M. Waiting for the revolution: How higher education institutions initially responded to ChatGPT. High. Educ. 2025, 89, 53–968. [Google Scholar] [CrossRef]
  62. Zhang, B. Preparing Educators and Students for Chatgpt and AI Technology in Higher Education; University of Hartford: West Hartford, CT, USA, 2023. [Google Scholar]
  63. Ulnicane, I.; Knight, W.; Leach, T.; Stahl, B.C.; Wanjiku, W.G. Framing governance for a contested emerging technology: Insights from AI policy. Policy Soc. 2021, 40, 58–177. [Google Scholar] [CrossRef]
  64. Lim, T.; Gottipati, S.; Cheong, M.L. Ethical considerations for artificial intelligence in educational assessments. In Creative AI Tools and Ethical Implications in Teaching and Learning; IGI Global: New York, NY, USA, 2023; pp. 32–79. [Google Scholar]
  65. Mahajan, P. What is Ethical: AIHED Driving Humans or Human-Driven AIHED? A Conceptual Framework enabling the Ethos of AI-driven Higher education. arXiv 2025, arXiv:2503.04751. [Google Scholar]
  66. Almarzouqi, A.; Aburayya, A.; Alfaisal, R.; Elbadawi, M.A.; Salloum, S.A. Ethical implications of using ChatGPT in educational environments: A comprehensive review. In Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom; Springer: Berlin/Heidelberg, Germany, 2024; pp. 185–199. [Google Scholar]
  67. Lloyd, K. Bias amplification in artificial intelligence systems. arXiv 2018, arXiv:1809.07842. [Google Scholar] [CrossRef]
  68. Noble, S.U. Algorithms of Oppression: How Search Engines Reinforce Racism; NYU Press: New York, NY, USA, 2018. [Google Scholar]
  69. Selwyn, N. Is Technology Good for Education? Polity Press: Cambridge, UK, 2016. [Google Scholar]
  70. West, S.M. Data Capitalism: Redefining the Logics of Surveillance and Privacy. Bus. Soc. 2017, 58, 20–41. [Google Scholar] [CrossRef]
  71. World Economic Forum, 2025. How AI and Human Teachers Can Collaborate to Transform Education. Available online: https://www.weforum.org/stories/2025/01/how-ai-and-human-teachers-can-collaborate-to-transform-education/ (accessed on 8 May 2025).
  72. Howard, A.; Borenstein, J. The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity. Sci. Eng. Ethic 2018, 24, 1521–1536. [Google Scholar] [CrossRef] [PubMed]
  73. Van Eck, N.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed]
Informatics 12 00074 g001
Figure 1. PRISMA flow chart.
Figure 1. PRISMA flow chart.
Informatics 12 00074 g001
Informatics 12 00074 g002
Figure 2. Occurrence of keywords in publications related to ChatGPT.
Figure 2. Occurrence of keywords in publications related to ChatGPT.
Informatics 12 00074 g002
Informatics 12 00074 g003
Figure 3. Countries that have published research papers on ChatGPT in higher education.
Figure 3. Countries that have published research papers on ChatGPT in higher education.
Informatics 12 00074 g003
Informatics 12 00074 g004
Figure 4. Number of publications on ChatGPT in education from 2022 to 2024.
Figure 4. Number of publications on ChatGPT in education from 2022 to 2024.
Informatics 12 00074 g004
Informatics 12 00074 g005
Figure 5. Top 10 research areas for ChatGPT publications.
Figure 5. Top 10 research areas for ChatGPT publications.
Informatics 12 00074 g005
Informatics 12 00074 g006
Figure 6. Word cloud based on abstract square root presentation.
Figure 6. Word cloud based on abstract square root presentation.
Informatics 12 00074 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Phokoye, S.P.; Dlamini, S.; Mthalane, P.P.; Luthuli, M.; Moyane, S.P. A Comprehensive Review of ChatGPT in Teaching and Learning Within Higher Education. Informatics 2025, 12, 74. https://doi.org/10.3390/informatics12030074

AMA Style

Phokoye SP, Dlamini S, Mthalane PP, Luthuli M, Moyane SP. A Comprehensive Review of ChatGPT in Teaching and Learning Within Higher Education. Informatics. 2025; 12(3):74. https://doi.org/10.3390/informatics12030074

Chicago/Turabian Style

Phokoye, Samkelisiwe Purity, Siphokazi Dlamini, Peggy Pinky Mthalane, Mthokozisi Luthuli, and Smangele Pretty Moyane. 2025. "A Comprehensive Review of ChatGPT in Teaching and Learning Within Higher Education" Informatics 12, no. 3: 74. https://doi.org/10.3390/informatics12030074

APA Style

Phokoye, S. P., Dlamini, S., Mthalane, P. P., Luthuli, M., & Moyane, S. P. (2025). A Comprehensive Review of ChatGPT in Teaching and Learning Within Higher Education. Informatics, 12(3), 74. https://doi.org/10.3390/informatics12030074

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop