Understanding ChatGPT adoption in universities: the impact of faculty TPACK and UTAUT2
DOI:
https://doi.org/10.5944/ried.28.1.41498Keywords:
Artificial Intelligence, TPACK, ChatGPT, UTAUT2 model, instructorsAbstract
The objective of the field of technology known as artificial intelligence (AI) is to create intelligent devices that can perform tasks that have traditionally required human intelligence. ChatGPT is a program based on AI that provide virtual instructors and personalized learning environment for students. It raises the bar for top performers by presenting cutting-edge information and encouraging intellectual development. This study aimed to investigate the significance of instructors' Technological Pedagogy Content Knowledge (TPACK) to determine the intention to use ChatGPT in the light of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The methodology of the study was a quantitative approach and the data was collected from 569 male and female instructors in Saudi universities. The data was analyzed by Path analysis and Smart PLS. The results of the study showed that Effort Expectancy, Social Influence, Hedonic Motivation, and Information Quality did not have a significant influence on Behavioral Intention. However, Facilitating Condition, Learning Value (negatively), and Privacy Risk have significant effects on Behavioral Intention. Moreover, there was a significant moderating role of Instructors' TPACK on the relationship between Privacy Risk and Behavioral Intention. The results shed a light on the effect of instructors’ TPACK and the lack of the relation among the three knowledge. Instructors’ TPACK should be improved with professional development programs in order to adapt a positive intention of using ChatGPT in Saudi universities. Universities are recommended to facilitate sufficient support and resources for the instructors to emerge new technology in their teaching.
Downloads
References
Alghatrifi, I., & Khalid, H. (2019). A systematic review of UTAUT and UTAUT2 as a baseline framework of information system research in adopting new technology: a case study of IPV6 adoption. 6th International Conference on Research and Innovation in Information Systems. IEEE. https://doi.org/10.1109/ICRIIS48246.2019.9073292
Al-Safadi, H. A., Shgair, M. S. A., & Al Qatawnih, K. S. (2023). The Effectiveness of Designing E-Learning Environment based on Mastery Learning and Artificial Intelligence on Developing English Speaking Skills among Tenth Graders in Palestine. Journal of Educational & Psychological Studies, 31(1). https://doi.org/10.33976/IUGJEPS.31.1/2023/22
Alshahrani, A. (2023). The impact of ChatGPT on blended learning: Current trends and future research directions. International Journal of Data and Network Science, 7(4), 2029-2040. https://doi.org/10.5267/j.ijdns.2023.6.010
Alzahrani, A. (2014). The Effects of Instructors’ Technological Pedagogical and Content Knowledge (TPACK) on Online Courses. Doctoral Dissertations. Texas Tech University. http://hdl.handle.net/2346/58720
Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended learning in medical education: application of UTAUT2 model. BMC Medical Education, 20, 1-9. https://doi.org/10.1186/s12909-020-02302-2
Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452. https://doi.org/10.1007/s10956-023-10039-y
Dai, Y., Liu, A., & Lim, C. P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. 33rd CIRP Design Conference, Sydney, Australia. 84-90. https://doi.org/10.1016/j.procir.2023.05.002
Dergaa, I., Chamari, K., Zmijewski, P., & Saad, H. B. (2023). From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport, 40(2), 615-622. https://doi.org/10.5114/biolsport.2023.125623
Eysenbach, G. (2023). The role of ChatGPT, generative language models, and artificial intelligence in medical education. JMIR Medical Education, 9(1), 1-13. https://doi.org/10.2196/46885
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human-Computer Interaction, 1-20. https://doi.org/10.1080/10447318.2023.2226495
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458. https://doi.org/10.1108/IMDS-04-2016-0130
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
Islam, I. e Islam, M. N. (2024). Exploring the opportunities and challenges of ChatGPT in academia. Discover Education, 3(1), 31. https://doi.org/10.1007/s44217-024-00114-w
Järvelä, S., Nguyen, A., & Hadwin, A. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057-1087. https://doi.org/10.1111/bjet.13325
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniak, G., Michaeli, T., Nerdel, C., Pfeffer, J., Paquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., ..., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103. https://doi.org/10.1016/j.lindif.2023.102274
Kopplin, C. S. (2022). Chatbots in the Workplace: A Technology Acceptance Study Applying Uses and Gratifications in Coworking Spaces. Journal of Organizational Computing and Electronic Commerce, 32(3-4), 232-257. https://doi.org/10.1080/10919392.2023.2215666
Li, S., Liu, Y., & Su, Y. S. (2022). Differential analysis of teachers’ technological pedagogical content knowledge (TPACK) abilities according to teaching stages and educational levels. Sustainability, 14(12), 7176. https://doi.org/10.3390/su14127176
Liu, M., Ren, Y., Nyagoga, L. M., Stonier, F., Wu, Z., & Yu, L. (2023). Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Educational Research. 1(1), 72-101. https://doi.org/10.1002/fer3.10
Luo, J. (2024). A critical review of GenAI policies in higher education assessment: A call to reconsider the “originality” of students’ work. Assessment & Evaluation in Higher Education, 1-14. https://doi.org/10.1080/02602938.2024.2309963
Medeiros, M., Ozturk, A., Hancer, M., Weinland, J., & Okumus, B. (2022). Understanding travel tracking mobile application usage: An integration of self determination theory and UTAUT2. Tourism Management Perspectives, 42. https://doi.org/10.1016/j.tmp.2022.100949
Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon, 9(11). https://doi.org/10.1016/j.heliyon.2023.e20962
Mohammad-Salehi, B., Vaez-Dalili, M., & Heidari Tabrizi, H. (2021). Investigating Factors That Influence EFL Teachers' Adoption of Web 2.0 Technologies: Evidence from Applying the UTAUT and TPACK. TESL-EJ, 25(1). https://tesl-ej.org/pdf/ej97/a21.pdf
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
Nookhao, S., & Kiattisin, S. (2023). Achieving a successful e-government: Determinants of behavioral intention from Thai citizens’ perspective. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18944
Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93. https://doi.org/10.1177/10776958221149577
Perera, P., & Lankathilaka, M. (2023). Preparing to Revolutionize Education with the Multi-Model GenAI Tool Google Gemini? A Journey towards Effective Policy Making. Journal of Advances in Education and Philosophy, 7(8), 246-253. https://doi.org/10.36348/jaep.2023.v07i08.001
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence‐based educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693-1710. https://doi.org/10.1111/bjet.12994
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Sharples, M. (2023). Towards social generative AI for education: theory, practices and ethics. Learning: Research and Practice, 9(2), 159-167. https://doi.org/10.1080/23735082.2023.2261131
Sidiropoulos, D., & Anagnostopoulos, C. N. (2024). Applications, challenges and ethical issues of AI and ChatGPT in education. arXiv preprint arXiv:2402.07907. https://doi.org/10.48550/arXiv.2402.07907
Smith, A., Hachen, S., Schleifer, R., Bhugra, D., Buadze, A., & Liebrenz, M. (2023). Old dog, new tricks? Exploring the potential functionalities of ChatGPT in supporting educational methods in social psychiatry. International Journal of Social Psychiatry, 69(8), 1882-1889. https://doi.org/10.1177/00207640231178451
Yilmaz, R., & Yilmaz, F. G. K. (2023). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2). https://doi.org/10.1016/j.chbah.2023.100005
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Abdulaziz Alzahrani, Amal Alzahrani
This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.
