Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education

Ramesh Kumar Moona Haji Mohamed; Nagaletchimee Annamalai; Yuvaraj Ganesan; Hermanto

doi:10.30935/cedtech/19637

Ramesh Kumar Moona Haji Mohamed ¹, Nagaletchimee Annamalai ² ^* , Yuvaraj Ganesan ³ ^* , Hermanto ⁴ ^*

More Detail

CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep699

https://doi.org/10.30935/cedtech/19637

Submitted: 18 May 2025, Published Online: 19 October 2026

OPEN ACCESS 34 Views 19 Downloads

Download Full Text (PDF)

Abstract

The rapid diffusion of generative artificial intelligence (GenAI), particularly large language models such as ChatGPT, has reshaped academic workflows while raising questions about trust, misconceptions, and future expectations. This study examines an integrated framework grounded in the unified theory of acceptance and use of technology and the technology acceptance model to assess how foundational acceptance drivers, cognitive evaluations, misconceptions, and trust are associated with academics’ forward-looking expectations of ChatGPT. Survey data from 252 academics in Malaysian public and private universities were analyzed using partial least squares structural equation modelling. Performance expectancy, effort expectancy, and facilitating conditions were positively associated with perceived ease of use (PEOU) or perceived usefulness (PU), while PEOU was positively associated with trust. Social influence was not significantly associated with PU. Two unexpected patterns were observed: PU was positively associated with misconceptions, and misconceptions were positively associated with trust. These cross-sectional associations suggest that functional appreciation can coexist with inflated capability beliefs and potentially miscalibrated trust, but they do not establish a causal mechanism of belief amplification. The Malaysian setting is analytically relevant because higher education institutions are expanding digitalization while national initiatives increasingly emphasize responsible AI governance, institutional capability, and digital skills. The findings highlight the importance of AI literacy, transparent institutional guidance, verification practices, and calibrated reliance when integrating GenAI into higher education.

Keywords: artificial intelligence, ChatGPT, academic perception, technology acceptance, structural equation modelling, higher education

References

Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). ChatGPT in teaching and learning: A systematic review. Education Sciences, 14(6), Article 643. https://doi.org/10.3390/educsci14060643
Ali, H., Ali, D., Fatemi, Y., & Bharadwaj, S. S. (2025). ChatGPT perceptions, experiences, and uses with emphasis on academia. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1559509
Amin, M. A., Kim, Y. S., & Noh, M. (2024). Unveiling the drivers of ChatGPT utilization in higher education sectors: The direct role of perceived knowledge and the mediating role of trust in ChatGPT. Education and Information Technologies, 30(6), 7265-7291. https://doi.org/10.1007/s10639-024-13095-y
Arbulú Ballesteros, M. A., Acosta Enríquez, B. G., Ramos Farroñán, E. V., García Juárez, H. D., Cruz Salinas, L. E., Blas Sánchez, J. E., Arbulú Castillo, J. C., Licapa-Redolfo, G. S., & Farfán Chilicaus, G. C. (2024). The sustainable integration of AI in higher education: Analyzing ChatGPT acceptance factors through an extended UTAUT2 framework in Peruvian universities. Sustainability, 16(23), Article 10707. https://doi.org/10.3390/su162310707
Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M. (2025). Determinants of ChatGPT adoption intention in higher education: Expanding on TAM with the mediating roles of trust and risk. Information, 16(2), Article 82. https://doi.org/10.3390/info16020082
Bhat, M. A., Tiwari, C. K., Bhaskar, P., & Khan, S. T. (2024). Examining ChatGPT adoption among educators in higher educational institutions using extended UTAUT model. Journal of Information, Communication and Ethics in Society, 22(3), 331-353. https://doi.org/10.1108/jices-03-2024-0033
Bhullar, P. S., Joshi, M., & Chugh, R. (2024). ChatGPT in higher education – A synthesis of the literature and a future research agenda. Education and Information Technologies, 29(16), 21501-21522. https://doi.org/10.1007/s10639-024-12723-x
Bikanga Ada, M. (2024). It helps with crap lecturers and their low effort: Investigating computer science students’ perceptions of using ChatGPT for learning. Education Sciences, 14(10), Article 1106. https://doi.org/10.3390/educsci14101106
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, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Cheah, J. H., Memon, M. A., Chuah, F., Ting, H., & Ramayah, T. (2018). Assessing reflective models in marketing research: A comparison between PLS and PLCc estimates. International Journal of Business and Society, 19(1), 139-160.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587
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
Deric, E., Frank, D., & Milkovic, M. (2025). Trust in generative AI tools: A comparative study of higher education students, teachers, and researchers. Information, 16(7), Article 622. https://doi.org/10.3390/info16070622
Diep, M. C., & Dang, M. H. (2025). Exploring factors influencing the adoption of ChatGPT as a supportive tool in EFL lesson creation. AsiaCALL Online Journal, 16(1), 111-139. https://doi.org/10.54855/acoj.251616
Eka, M., Asih, M. S., Damayanti, F., Saragih, R., & Supiyandi, S. (2025). A narrative exploring the potential of ChatGPT: How AI models are changing the way we interact with technology. Journal of Computer Science Artificial Intelligence and Communications, 1(1), 24-29. https://doi.org/10.64803/jocsaic.v1i1.5
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Foroughi, B., Iranmanesh, M., Ghobakhloo, M., Senali, M. G., Annamalai, N., Naghmeh-Abbaspour, B., & Rejeb, A. (2024). Determinants of ChatGPT adoption among students in higher education: The moderating effect of trust. The Electronic Library, 43(1), 1-21. https://doi.org/10.1108/el-12-2023-0293
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/ebr-11-2018-0203
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/imds-09-2015-0382
Hoffmann, A. O. I., & Birnbrich, C. (2012). The impact of fraud prevention on bank-customer relationships: An empirical investigation in retail banking. International Journal of Bank Marketing, 30(5), 390-407. https://doi.org/10.1108/02652321211247435
Karkoulian, S., Sayegh, N., & Sayegh, N. (2024). ChatGPT unveiled: Understanding perceptions of academic integrity in higher education – A qualitative approach. Journal of Academic Ethics, 23(3), 1171-1188. https://doi.org/10.1007/s10805-024-09543-6
Khan, R., Qamar, M. T., Ansari, M. S., & Yasmeen, J. (2025). Enhancing or impairing? Exploring Indian EFL learners’ academic writing narratives with ChatGPT. Cogent Education, 12(1), Article 2514329. https://doi.org/10.1080/2331186x.2025.2514329
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(1), 50-80. https://doi.org/10.1518/hfes.46.1.50_30392
Liang, C.-C., & Shiau, W.-L. (2018). Moderating effect of privacy concerns and subjective norms between satisfaction and repurchase of airline e-ticket through airline-ticket vendors. Asia Pacific Journal of Tourism Research, 23(12), 1142-1159. https://doi.org/10.1080/10941665.2018.1528290
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362-392. https://doi.org/10.1111/deci.12445
Lijie, H., Mat Yusoff, S., & Mohamad Marzaini, A. F. (2024). Influence of AI-driven educational tools on critical thinking dispositions among university students in Malaysia: A study of key factors and correlations. Education and Information Technologies, 30(6), 8029-8053. https://doi.org/10.1007/s10639-024-13150-8
Malik, A., Khan, M. L., Hussain, K., Qadir, J., & Tarhini, A. (2025). AI in higher education: Unveiling academicians’ perspectives on teaching, research, and ethics in the age of ChatGPT. Interactive Learning Environments, 33(3), 2390-2406. https://doi.org/10.1080/10494820.2024.2409407
Ministry of Higher Education Malaysia. (2025). Digitalisation of higher education action plan 2025-2030. Ministry of Higher Education Malaysia. https://www.mohe.gov.my/en/download/publications-journals-and-reports/pelan-tindakan-pendigitalan-pendidikan-tinggi-2025-2030
Ministry of Science, Technology and Innovation Malaysia. (2024). National guidelines on AI governance and ethics. Ministry of Science, Technology and Innovation Malaysia. https://mastic.mosti.gov.my/publication/the-national-guidelines-on-ai-governance-ethics
Paradeda, R. B., Torres, D. T. B., & Takahashi, A. (2025). Generative AI in education: A study of undergraduate students’ expectations of ChatGPT. RENOTE, 22(3), 174-185. https://doi.org/10.22456/1679-1916.144982
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253. https://doi.org/10.1518/001872097778543886
Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467-480. https://doi.org/10.1016/j.jom.2012.06.002
Seow, A. N., Chan, M. S., Tan, C. H., & Yap, Y.-Y. (2025). Sustainable business performance of small and medium-sized enterprises in green hotels: The mediating role of competitive advantage. Cogent Business & Management, 12(1), Article 2526144. https://doi.org/10.1080/23311975.2025.2526144
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21, Article 46. https://doi.org/10.1186/s41239-024-00478-x
Shaked, D., Faulkner, L. M. D., Tolle, K., Wendell, C. R., Waldstein, S. R., & Spencer, R. J. (2020). Reliability and validity of the Conners’ continuous performance test. Applied Neuropsychology: Adult, 27(5), 478-487. https://doi.org/10.1080/23279095.2019.1570199
Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision making? International Journal of Human Computer Studies, 51(5), 991-1006. https://doi.org/10.1006/ijhc.1999.0252
Subhani, F., Khan, S. A., Sandhu, M. A., & Shahzad, M. F. (2025). What factors affect the adoption intention and actual use of ChatGPT in higher education? The moderating role of academic integrity. TechTrends, 69(5), 1056-1071. https://doi.org/10.1007/s11528-025-01096-8
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. https://doi.org/10.1016/j.csda.2004.03.005
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., Wasson, B., Tømte, C., Spikol, D., Milrad, M., Coelho, R., & Kizilcec, R. F. (2024). What explains teachers’ trust in AI in education across six countries? International Journal of Artificial Intelligence in Education, 35(3), 1288-1316. https://doi.org/10.1007/s40593-024-00433-x
Zhao, Y., Li, Y., Xiao, Y., Chang, H., & Liu, B. (2024). Factors influencing the acceptance of ChatGPT in high education: An integrated model with PLS-SEM and fsQCA approach. Sage Open, 14(4). https://doi.org/10.1177/21582440241289835

Citation

Moona Haji Mohamed, R. K., Annamalai, N., Ganesan, Y., & Hermanto (2026). Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education. Contemporary Educational Technology, 18(4), Article ep699. https://doi.org/10.30935/cedtech/19637

Moona Haji Mohamed, R. K., Annamalai, N., Ganesan, Y., and Hermanto (2026). Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education. Contemporary Educational Technology, 18(4), ep699. https://doi.org/10.30935/cedtech/19637

Moona Haji Mohamed RK, Annamalai N, Ganesan Y, Hermanto. Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education. CONT ED TECHNOLOGY. 2026;18(4), ep699. https://doi.org/10.30935/cedtech/19637

Moona Haji Mohamed, Ramesh Kumar, Nagaletchimee Annamalai, Yuvaraj Ganesan, and Hermanto. "Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education". Contemporary Educational Technology 2026 18 no. 4 (2026): ep699. https://doi.org/10.30935/cedtech/19637

Moona Haji Mohamed, Ramesh Kumar et al. "Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education". Contemporary Educational Technology, vol. 18, no. 4, 2026, ep699. https://doi.org/10.30935/cedtech/19637

Moona Haji Mohamed RK, Annamalai N, Ganesan Y, Hermanto. Miscalibrated trust: How academic perceptions and misconceptions shape ChatGPT adoption in higher education. CONT ED TECHNOLOGY. 2026;18(4):ep699. https://doi.org/10.30935/cedtech/19637