Faculty Adoption of Generative Artificial Intelligence in a Canadian Higher Education Institution
Chip processing and generating data
PDF

Keywords

Higher education
instructors
generative artificial intelligence
Canada
adopting AI
pedagogy
teaching and learning

How to Cite

Miron, J., Karam, M., & Kiranda, H. K. (2025). Faculty Adoption of Generative Artificial Intelligence in a Canadian Higher Education Institution. Journal of Innovation in Polytechnic Education, 7(1), 27–37. https://doi.org/10.69520/jipe.v7i1.245

Abstract

The landscape of higher education (HE) continues to change rapidly with the incorporation of new artificial intelligence (AI) applications like generative artificial intelligence (genAI). These transformations can be attributed to the ubiquity, efficacy, and quality of genAI applications. GenAI will necessitate the need for HE instructors to adapt and use these technologies to sustain and enhance student learning. This paper reports quantitative findings influencing instructors’ intentions to adopt genAI into their pedagogies. The Artificial Intelligence Acceptance Measurement Survey (AIAMS) was developed and adapted from the revised Technology Acceptance Model Survey-2 (TAMS-2) that incorporates the main constructs from the Theory of Planned Behavior (TPB). The survey was administered to a sample of instructors from different programs working in a large Canadian urban polytechnic institution (n=87). Multiple regression analysis was conducted to identify the main determinants influencing instructors’ intention to adopt genAI in their teaching. Statistical findings reveal that instructors’ attitudes toward genAI were the only significant factor influencing their intent to adopt it in their teaching practices. It is crucial for those in HE to understand the factors that influence instructors’ intentions to integrate genAI into their teaching practices to support and realize its successful adoption. This understanding is also key for leveraging the full potential and capabilities of genAI to enhance educational outcomes.

https://doi.org/10.69520/jipe.v7i1.245
PDF

References

Abdelmoneim, R., Jebreen, K., Radwan, E., & Kammoun-Rebai, W. (2024). Perspectives of teachers on the employ of educational artificial intelligence tools in education: The case of the Gaza Strip, Palestine. Human Arenas, 1-30.

Ajzen, I., (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed.). Berkshire, UK: Open University Press McGraw-Hill Publication.

Ajzen, I. (2006). Constructing a TpB questionnaire: Conceptual and methodological considerations. http://www.unibielefeld.de/ikg/zick/ajzen%20construction%20a%20tpb%20questionnaire.pdf

Ajzen, I. (2020). The Theory of Planned Behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314.324.

Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-Covid era and its impact on faculty members' occupational well-being and teaching self-efficacy: A path analysis using the UTAUT 2 Model. (Unified Theory of Acceptance and Use of Technology). Applied Artificial Intelligence, 37(1), 2175110. https://doi.org/10.1080/08839514.2023.2175110

Ancion, A., Klein, A., DiNello, M., & Shi, L. (n.d.). Generative AI and higher education. Anticipating, creating, and shaping a better post-secondary system. Deloitte Higher Education. https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/deloitte-analytics/ca-generative-ai-and-higher-education-en.pdf?utm_source=chatgpt.com

Armitage, C.J., & Conner, M. (2001) Efficacy of the Theory of Planned Behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499.

Attard-Frost, B., Brandusecu, A., & Lyons, K. (2024). The governance of artificial intelligence in Canada: Findings and opportunities from a review of 84 AI governance initiatives. Government Information Quarterly, 41, 101929, 1-24. www.elsevier.com/locate/govinf

Bahn, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(63), 1-17. https://doi.org/10.1007/s122-023-00680-1

Bezjak, S. (2024). Perceptions and perspectives: Understanding teachers’ attitudes toward AI in education. Development, 23(25), 399-406.

Bordas, A., Masson, P., Thomas, M., & Weil, B. (2024). What is generative in generative artificial intelligence? A design-based perspective. Research in Engineering Design, 35, 427-443. https://doi.org/10.1007/s00163-024-00441-x

Cheon, J., Lee, S., Crooks, S., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 104-1064. https://doi.org/10.1016/j.compedu.2012.04.015

Chou, CM., Shen, TC., Shen, TC., & Shen CH. (2024). Teachers’ adoption of AI-supported teaching behavior and its influencing factors: using structural equation modeling. Journal of Computers in Education. https://doi.org/10.1007/s40692-024-00332-z

Cojean, S., Brun, L., Amadieu, F., & Dessus, P. (2023). Teachers’ attitudes toward AI: What is the difference with non-AI technologies? In M. Goldwater, F.K. Anggoro, B.K. Hayes, & D.C. Ong (Eds.) Proceedings of the 4th Annual Conference of the Cognitive Science Society. https://escholarship.org/uc/item/0r55s1jb#main

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.

Farrelly, T., & Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109

Feng, G., Su, X., Lin, Z., He, Y., Luo, N., & Zhang, Y. (2020). Determinants of technology acceptance: Two model-based meta-analytic reviews. Journalism & Mass Communication Quarterly, 98(1), 83-104. https://doi.org/10.1177/1077699020952400

Galindo-Domínquez, H., Delgado, N., Campo, L., & Losado, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 124(102381). https://doi.org/10.1016/j.ijer.2024.10239

Gupta, K. P., & Bhaskar, P. (2020). Inhibiting and motivating factors influencing teachers’ adoption of AI-Based teaching and learning solutions: Prioritization using analytic hierarchy process. Journal of Information Technology Education Research, 19, 693-723. https://doi.org/10.28945/4640

Haddock, G., & Maio, G. (2004). Contemporary perspectives on the psychology of attitudes. New York, Psychology Press, Taylor & Francis Group.

Hagger, M.S., Cheung, M.W., Ajzen, I., & Hamilton, K. (2022). Perceived behavioural control moderating effects in the Theory of Planned Behaviour: A meta-analysis. Health Psychology, 41(2), 155-167.

Hagger, M., Hamilton, K., Ajzen, I., Bosnjak, M., & Schmidt, P. (2021). Testing the replicability of the Theory of Planned Behavior: A large-scale multi sample registered replication study. PsychArchives, 14(10), 21. https://doi.org/10.23668/PSYCHARCHIVES.4807

Hagger, M. & Chatzisarantis, N. (2010). Integrating the Theory of Planned Behaviour and Self- Determination Theory in health behaviour: A meta-analysis. British Journal of Health Psychology, 14(2), 275-302. https://doi.org/10.1348/135910708X373959

Harris, P. (2024). Faculty perspectives toward artificial intelligence in higher education [Unpublished doctoral dissertation]. Middle Georgia State University.

Ivanov, S., Soliman, M., Tuomi, A., Akathiri, N., & Al-Alawi, A. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behavior. Technology in Society, 77(102521). http://doi.org/10.1016/j.techsoc.2024.102521

Khathayut, P., Walker-Gleaves, C., & Humble, S. (2022). Using the theory of planned behaviour to understand Thai students’ conceptions of plagiarism within their undergraduate programmes in higher education. Studies in Higher Education, 47(2), 394-411. https://doi.org/10.1080/03075079.2020.170584

Luckin, R., Cukorova, M., Kent, C., du Boulay, B. (2022). Empowering educators to be AI-ready. Computers and Education: Artificial Intelligence, 3(100076), 1-11. https://doi.org/10.1016.j.caeai.2022.100076

Marcel, F. & Kang, P. (2024). Examining AI guidelines in Canadian universities: Implications on academic integrity in academic writing. Discourse and Writing, 34, 93-126. https://journals.sfu.ca/dwr

Mollman, S. (2022, December 9). ChatGPT gained 1 million users in under a week. Here’s why the AI chatbot is primed to disrupt search as we know it. Fortune. https://finance.yahoo.com/news/chatgpt-gained-1-million-followers-224523258.html?fr=yhssrp_catchall

Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021, September 20). Confirmation bias and trust: Human factors influence teachers’ attitudes towards AI-based educational technology [Paper]. AI for Blending Learning: Empowering Teachers in Real Classrooms. Bozeno-Bolzano, Italy.

Nunnally, J.C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.

Nyamekye, E. (2024). Indigenous language learning in higher education in Ghana: Exploring students’ behavioural intentions using an extended theory of planned behavior. PLOS One, 19(6), 1-19, https://doi.org/10.1371/journal.pone.0304390

Ofosu-Ampong, K. (2024). Beyond the hype: Exploring faculty perceptions and acceptability of AI in teaching practices. Discover Education, 3:38. https://doi.org/10.1007/s44217-024-00128-4

Priya, G. & Preeti, B. (2020). Inhibiting and motivating factors influencing teachers’ adoption of AI-based teaching and learning solutions: Prioritization using analytic hierarchy process. Journal of Information Technology. 19, 693-712.

Shah, P. (2023). Embracing AI in education. In AI and the Future of Education. Teaching in the Age of Artificial Intelligence. Jossey-Bass.

Shi, L., Ding, A.-C. E., & Choi, I. (2024). Investigating teachers’ use of an AI-enabled system and their perceptions of AI integration in science classrooms: A case study. Education Sciences, 14(11), 1187. https://doi.org/10.3390/educsci14111187

Teng, M., Singla, R., Yau, O., Lamoureux, D., Gupta, A., Hu, Z., Hu, R., Aissiou, A., Eaton, S., Hamm, C., Hu, S., Kelly, D., MacMillan, K., Malik, S., Mazzoli, V., Teng, Y., Laricheva, M., Jarus, T., Field, T. (2022). Health care students’ perspectives on artificial intelligence: Countrywide survey in Canada. JMIR Med Educ, 8(1): e33390, doi.2196/33390

Uyanik, A., Kasapoglu, K., & Aydogdu, B. (2024). Comparing Turkish pre-service stem and non-stem teachers’ attitudes and anxiety toward artificial intelligence. Journal of Baltic Science Education, 23(5), 950-963. doi.10.3322/jbse/24.23950

Venkatesh, V. & Davis, F. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Wang, Y, Liu, C., Tu, Y.-F. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers’ perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116–129.

Yim, I. H. Y., & Wegerif, R. (2024). Teachers' perceptions, attitudes, and acceptance of artificial intelligence educational learning tools: An exploratory study on artificial intelligence literacy for young students. Future in Educational Research, 1–28. https://doi.org/10.1002/fer3.65

Yue, M., Jong, M., & Ng, D. (2024). Understanding K-12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 29, 19505-19536. https://doi.org/10.1007/s10639-024-12621-2

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Downloads

Download data is not yet available.