MAPPING THE LANDSCAPE OF GENERATIVE AI ACCEPTANCE IN ACADEMIC WRITING: A BIBLIOMETRIC REVIEW OF THE EXTENDED TAM RESEARCH
DOI:
https://doi.org/10.35631/IJMOE.831030Keywords:
Academic Writing, Bibliometric Analysis, Generative Artificial Intelligence, Higher Education, Technology AcceptanceAbstract
Academic writing in higher education has been reshaped within a remarkably short period by ChatGPT and comparable large language models (LLMs). This review asks how that shift has been studied: what the scholarly record so far reveals about university students’ willingness to take up such tools, and how the Technology Acceptance Model (TAM) has been stretched to account for it. Fifteen peer-reviewed studies appearing between 2023 and 2025 were assembled and examined through bibliometric profiling alongside qualitative content analysis, each article coded against a shared set of extraction fields. Output rose steeply across the three-year window, mirroring the wave of interest that followed ChatGPT’s public release. TAM proved the framework of choice, frequently merged with the Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2) or the Theory of Planned Behaviour (TPB). Constructs recurring across the corpus were Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioural Intention (BI) and, distinctively for this domain, Academic Integrity Concern (AIC). Among the ten most cited papers the prevailing design was a cross-sectional survey analysed with Partial Least Squares Structural Equation Modelling (PLS-SEM), with authorship concentrated in Malaysia, the United States, Norway, Poland, Turkey and China. Two patterns stand out. Growth in output has outpaced growth in methodological variety, and AIC behaves less like a fixed construct than one contingent on institutional policy. The review therefore offers postgraduate researchers, educators and policymakers a mapped starting point for governing how GenAI enters academic writing.
Downloads
References
Abdullah, K. H. (2022). Publication Trends in Biology Education: A Bibliometric Review of 63 Years. Journal of Turkish Science Education, 19(2), 465–480. https://doi.org/10.36681/tused.2022.131
Abdullah, K. H., Roslan, M. F., & Ilias, M. (2023). A bibliometric analysis of literature review articles published by Malaysian authors. Jurnal Penyelidikan Sains Sosial (JOSSR), 6(6), 8–26. https://doi.org/10.55573/JOSSR.061802
Bittle, K., & El-Gayar, O. (2025). Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. In Information (Switzerland) (Vol. 16, Number 4). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/info16040296
Cao, C., Li, Z., Ni, M., Luo, F., & Ling, C. (2025). Will human-like features affect the adoption of generative AI tools? A study in Chinese University students. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1673150
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1). https://doi.org/10.3390/soc15010006
Grassini, S., Aasen, M. L., & Møgelvang, A. (2024). Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model. Applied Artificial Intelligence, 38(1). https://doi.org/10.1080/08839514.2024.2371168
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77. https://doi.org/10.1016/j.techsoc.2024.102521
Kim, J., Lee, S. S., Detrick, R., Wang, J., & Li, N. (2025). Students-Generative AI interaction patterns and its impact on academic writing. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-025-09444-6
Maxwell, D., Oyarzun, B., Kim, S., & Bong, J. Y. (2025). Generative AI in Higher Education: Demographic Differences in Student Perceived Readiness, Benefits, and Challenges. TechTrends, 69(6), 1248–1259. https://doi.org/10.1007/s11528-025-01109-6
Meyer, J. G., Urbanowicz, R. J., Martin, P. C. N., O’Connor, K., Li, R., Peng, P. C., Bright, T. J., Tatonetti, N., Won, K. J., Gonzalez-Hernandez, G., & Moore, J. H. (2023). ChatGPT and large language models in academia: opportunities and challenges. In BioData Mining (Vol. 16, Number 1). BioMed Central Ltd. https://doi.org/10.1186/s13040-023-00339-9
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025). Students’ Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language †. Education Sciences, 15(5). https://doi.org/10.3390/educsci15050611
Sousa, A. E., & Cardoso, P. (2025). Use of Generative AI by Higher Education Students. Electronics (Switzerland), 14(7). https://doi.org/10.3390/electronics14071258
Strzelecki, A. (2024). Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education, 49(2), 223–245. https://doi.org/10.1007/s10755-023-09686-1
Türk, N., Batuk, B., Kaya, A., & Yıldırım, O. (2025). What makes university students accept generative artificial intelligence? A moderated mediation model. BMC Psychology, 13(1). https://doi.org/10.1186/s40359-025-03559-2Vieriu, A. M., & Petrea, G. (2025). The Impact of Artificial Intelligence (AI) on Students’ Academic Development. Education Sciences, 15(3). https://doi.org/10.3390/educsci15030343
