Exploring Private College Lecturers’ Intentions and Strategies in using Generative AI Tools: A Case Study
DOI:
https://doi.org/10.11113/sh.v18n3.2330Keywords:
Generative AI; Lecturers’ Intentions; Strategies; Technology Acceptance ModelAbstract
In line with the rapid development of technology, lecturers in higher education institutions are expected to use technological tools such as Artificial Intelligence (AI) to enhance their teaching strategies and work productivity. This qualitative case study aimed to identify lecturers’ perceptions on the usefulness and ease of use of generative AI tools for their academic work. Moreover, their long-term intention and strategies in relation to the usage of those tools were also investigated. Technology Acceptance Model (TAM) was applied in this study. The participants of this research study were three lecturers from a private college in Johor Bahru. They provided their responses individually in semi-structured interviews. The data was then transcribed and analysed with thematic analysis method. All the participants stated that generative AI tools were generally useful and easy to use. However, one of them explained that the tools may be more challenging to use for more complex usage. The three lecturers also expressed their intention to use the tools in the long term, and they provided some effective strategies in obtaining relevant AI output to complete their tasks. However, some participants explained that overdependency on the generative AI tools may become a concerning issue among lecturers. It is hoped that these research findings will be useful to educators and management in the higher education setting. Continuous professional development in relation to the usage of generative AI tools may be planned for equipping lecturers with skills to maximise the benefits of using those tools in the higher education field.
References
Abdullah, Z., & Mohd Zaid, N. (2023). Perception of generative artificial intelligence in higher education research. Innovative Teaching and Learning Journal, 7(2), 84–95. DOI: https://doi.org/10.11113/itlj.v7.137
Ahmed, S. K., Mohammed, R. A., Nashwan, A. J., Ibrahim, R. H., Abdalla, A. Q., Ameen, B. M. M., & Khidhir, R. M. (2025). Using thematic analysis in qualitative research. Journal of Medicine, Surgery, and Public Health, 6, 1-6. DOI: https://doi.org/10.1016/j.glmedi.2025.100198
Amir-Rudin, A., Abdul Hamid, A. H., Rosmail, I. I., Abu Seman, S. A., & Abd Rashid, N. (2025). Validating the acceptance of artificial intelligence (AI) in higher education institutions using the technology acceptance model (TAM). Environment-Behaviour Proceedings Journal, 10(SI26), 137–144. DOI: https://doi.org/10.21834/e-bpj.v10isi26.6810
Boyle, C. (2025). ChatGPT, Gemini, & Copilot: Using generative AI as a tool for information literacy instruction. The Reference Librarian, 66(1-2), 1–17. DOI: https://doi.org/10.1080/02763877.2025.2465416
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. DOI: https://doi.org/10.1191/1478088706qp063oa
Burgess, G. L., Worthington, A. K. (2021). Technology Acceptance Model. In Worthington (Ed.), Persuasion Theory in Action: An Open Educational Resource. University of Alaska Anchorage. https://ua.pressbooks.pub/persuasiontheoryinaction/chapter/technology-acceptance-model/ Retrieved on May 20, 2025 f
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. DOI: https://doi.org/10.2307/249008
Eager, B., & Brunton, R. (2023). Prompting higher education towards AI-augmented teaching and learning practice. Journal of University Teaching & Learning Practice, 20(5). DOI: https://doi.org/10.53761/1.20.5.02
ElSayary, A., Kuhail, M. A., & Hojeij, Z. (2025). Examining the role of prompt engineering in utilizing generative AI tools for lesson planning: Insights from teachers’ experiences and perceptions. Human Behavior and Emerging Technologies. 2025(1), 1-21.DOI: https://doi.org/10.1155/hbe2/9986139
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. DOI: https://doi.org/10.1007/s12599-023-00834-7
Huma, T., Ahmed, S., Mahmood, W., & Afridi, A. K. (2025). AI adoption in higher education: A comparative study of institutional readiness and challenges. Social Science Review Archives, 3(4), 304-312. https://policyjournalofms.com/index.php/6/article/view/1117
Kavitha, K., & Joshith, V. P. (2025). Artificial intelligence powered pedagogy: Unveiling higher educators acceptance with extended TAM. Journal of University Teaching and Learning Practice, 21(8), 1-34. DOI: https://doi.org/10.53761/s1pkk784
Krause, S., Dalvi, A., & Zaidi, S. K. (2025). Generative AI in education: Student skills and lecturer roles. ArXiv.org. https://arxiv.org/abs/2504.19673
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 1-10. DOI: https://doi.org/10.1016/j.caeai.2024.100221
Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: a systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(7), 1-22. DOI: https://doi.org/10.1186/s41239-025-00503-7
Lim, W. M. (2024). What is qualitative research? An overview and guidelines. Australasian Marketing Journal. 33(2), 199-229. https://journals.sagepub.com/doi/10.1177/14413582241264619
Mijan, A., Hasan, M.R., & Hasan, M. (2025). AI and academia: Navigating the adoption of artificial intelligence in universities. International Journal of Technology in Education and Science (IJTES), 9(1), 54- 65. DOI: https://doi.org/10.46328/ijtes.602
Miranda, F. J., & Chamorro-Mera, A. (2026). Exploring the adoption of generative artificial intelligence tools among university teachers. Higher Education Research & Development, 45(3), 680-696. DOI: https://www.tandfonline.com/doi/full/10.1080/07294360.2025.2559648
Nazary, F., Deldjoo, Y., & Noia, T. D. (2024). ChatGPT-HealthPrompt. Harnessing the power of XAI in prompt-based healthcare decision support using ChatGPT. Artificial Intelligence. ECAI 2023 International Workshops (ECAI 2023), 382–397. DOI: https://doi.org/10.1007/978-3-031-50396-2_22
Ooi, K., Tan, G. W., Al-Emran, M., Al-Sharafi, M. A., Căpăţînă, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-W., Kar, A. K., Lee, V., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., & Teng, C. (2023). The potential of generative artificial intelligence across disciplines: perspectives and future directions. Journal of Computer Information Systems, 65, 76–107. DOI: https://doi.org/10.1080/08874417.2023.2261010
Park, J., & Choo, S. (2024). Generative AI prompt engineering for educators: Practical strategies. Journal of Special Education Technology, 40(3), 411-417. DOI: https://doi.org/10.1177/01626434241298954
Robinson, A. (2025). Generative artificial intelligence in higher education: Understanding faculty adoption through the technology acceptance model. I-Manager’s Journal of Educational Technology, 22(1), 18–31. DOI: https://doi.org/10.26634/jet.22.1.21796
Ruslin, Mashuri, S., Abdul Rasak, M. S., Alhabsyi, F., & Syam, H. (2022). Semi-structured interview: A methodological reflection on the development of a qualitative research instrument in educational studies. IOSR Journal of Research & Method in Education (IOSR-JRME), 12(1), 22–29. https://www.iosrjournals.org/iosr-jrme/papers/Vol-12%20Issue-1/Ser-5/E1201052229.pdf
Schoch, K. (2020). Case study research. Research design and methods: An applied guide for the scholar-practitioner, 31(1), 245-258. https://www.researchgate.net/profile/Subhash Basu 3/post/How_do_i_determine_the_sample_size_for_a_study_looking_at_the_treatment_outcomes_of_mental_health_patients_in_a_community_house/attachment/5ebbae3eead4db0001551c21/AS:890646755811328@1589358142328/download/105275_book_item_105275.pdf
Shata, A., & Hartley, K. (2025). Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI. International Journal of Educational Technology in Higher Education, 22(1), 14. https://link.springer.com/article/10.1186/s41239-025-00511-7
Stratton, S. J. (2024). Purposeful sampling: advantages and pitfalls. Prehospital and Disaster Medicine, 39(2), 121–122. DOI: https://doi.org/10.1017/S1049023X24000281
Varghese, J., & Chapiro, J. (2023). ChatGPT: the transformative influence of generative AI on science and healthcare. Journal of Hepatology, 80(6), 977–980, DOI: https://doi.org/10.1016/j.jhep.2023.07.028, 37544516.
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(2024), 1-29. DOI: https://doi.org/10.1186/s41239-024-00448-3
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. ArXiv.org. https://arxiv.org/abs/2302.11382














