Critical review of ChatGPT in higher education: A systematic literature review and research challenges
Critical review of ChatGPT in higher education
A systematic literature review and research challenges
Introduction
The rapid evolution of generative AI tools has drastically changed higher education and has drawn a lot of attention from scholars who are now considering the tools’ pedagogical, ethical, and institutional ramifications. Baig and Yadegaridehkordi (2024) in the International Journal of Educational Research, undertake a very proper and thorough systematic literature review (SLR) on the adoption, applications, and limitations of ChatGPT in higher education. The author is in a fast-changing research environment and dealing with issues such as academic integrity, technology acceptance, and the pedagogical worth of AI-powered tools in universities across the world.
The research proposes three objectives: the first being to identify recent research areas related to ChatGPT in higher education, the second to evaluate the adoption and usage patterns according to the theoretical models, and the last to combine the varied applications and shortcomings that are already mentioned in the literature. In this case, the article intends mainly to address the researchers, academic practitioners, and institutional decision-makers who seek evidence-based insights into the ChatGPT integration in higher education.
Summary of the Article
The authors methodologically take a systematic literature review approach in accordance to Kitchenham and Charters’ protocol, scrutinising 57 peer-reviewed articles that have been published between January 2023 and the middle of January 2024. The review gives a detailed synthesis of the research trends according to time distribution, countries covered, methods used, and citation frequencies. The results indicate a drastic rise in the number of articles related to ChatGPT which shows the increasing interest of the world in the application of generative AI in education
The paper’s major contribution is its thorough scrutiny of user behaviour and the adoption process. Among other things, the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and UTAUT2 are highlighted as the most popular theoretical frameworks used in investigating ChatGPT adoption. The factors perceived as useful, social influence, effort expectancy, and facilitating conditions come up as the main predictors of not only adoption but also the continued usage of the technology.
Moreover, the research interprets ChatGPT usage in terms of different stakeholder groups comprising academic staff, students, researchers, and non-academic personnel. Applications for these groups stretch from assessment creation and teaching monitoring to customised learning, research assistance, office work automation, and writing. The paper ends with a discussion of present research shortcomings and suggestions for future research, which are mainly concerning methodological variety, moral issues, and adoption at the institutional level.
Critique
Significance and Contribution to the Field
The article’s thorough and transparent methodological synthesis of the initial research about ChatGPT in higher education is among the most remarkable strengths of the article. The research, through systematic mapping of trends, applications, and theoretical approaches, offers a significant baseline for foreshadowing in the fields of educational technology and AI-capable learning environments. Its all-inclusive treatment of various stakeholder groups gives it a breadth of significance that surpasses that of the student-centric adoption studies.
Additionally, the article comes up with a considerable solution by fusing dispersed results into a single research agenda. By this, it assists in the overall decision-making process of the educators and policymakers who are integrating the generative AI tools in the academic institutions and are in need of the support of the evidence.
Methodology and Research Design
The systematic literature review methodology is strict and well-documented, following established SLR standards. The authors are very clear about what studies to include or exclude, and the application of quality assessments makes the results even more credible. The presentation of the selection process in a visual form also promotes transparency to a greater extent.
On the other hand, the review has to cope with the fact that it is based on a rather short time frame (2023–2024). Even though this is a limitation, it can be justified by the novelty of ChatGPT, and thus, it is not entirely lacking in longitudinal insights. Besides, the exclusion of conceptual and editorial pieces may have left out important normative and theoretical viewpoints in a new research domain that is still developing.
Argumentation and Use of Evidence
The article is well-organised, and the author’s main points are easy to follow, moving from trends to mechanisms of adoption, applications, and limitations. Diagrams and tables are used to a large extent, which makes the article easier to read and understand. However, the synthesis is predominantly descriptive with very little critical analysis of contradictions or conflicts within the literature.
In addition, the writers point out the prevailing theoretical models but do not critically evaluate if these models are completely sufficient for giving an account of the societal and technical complexity of generative AI adoption in education.
Ethical Considerations and Omissions
All the ethical matters, such as plagiarism, data privacy, and academic integrity, are still recognised; however, they are considered mainly as technical difficulties rather than deep institutional or epistemological problems. If the article had addressed more critically the larger governance, power asymmetries, and policy implications, which are all interlinked to AI in the educational sector, it would have been a significant gain.
In the same way, the institutional aspects like incentive systems, faculty training, and regulatory preparedness, which are indispensable for continuous AI integration, have received little attention, and therefore, their importance is still underestimated.
Writing Style and Structure
The clarity, logical arrangement, and accessibility of the writing are aimed at an academic audience. The presence of subheadings and the systematic organisation of the content play a significant role in making the text easier to understand. Nevertheless, the predominantly neutral style restricts the level of criticism to some extent, especially in parts dealing with limitations and future research directions.
Conclusion
Baig and Yadegaridehkordi (2024) present a very useful review of ChatGPT application in colleges with great methodology and at the right moment, hence contributing a lot to the already large literature on AI-based education technologies. The literature review not only uncovers the trends in research, the pathways of adoption, the use of the technology and the drawbacks, but also sets up a very solid platform for further empirical and theoretical investigations.
Nonetheless, the strategy used by the writers to show the current situation instead of through the lens of critical theorisation results in the articles not being able to influence the prevailing paradigms of technology diffusion and educational innovations. Subsequent research can regard this review as an initial point and further incorporate long-term studies, institutional critiques, and moral as well as governance issues discussions. In conclusion, the article signifies a very critical moment in the preliminary comprehension of ChatGPT and its role in higher education, while concurrently emphasising the need for profound and critical interaction with generative AI technologies.
Reference:
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411.https://doi.org/10.1016/j.ijer.2024.102411
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