Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory
Open accessAbstract
There is a need for teachers who are prepared to teach Artificial Intelligence (AI) across the K-12 learning contexts. Owing to the dearth of teacher education programmes on AI, it is helpful to explore factors to be considered in designing an effective AI programme for future teachers. We posit that understanding how to encourage pre-service teachers to learn AI is thus critical for practitioners and policymakers while designing effective instructional AI teacher education programmes. This exploratory study examined the perceptions of pre-service teachers and their behavioral intention to learn AI, by identifying factors that might affect learning and promoting AI in teacher preparation programmes. This study proposed a research model supported by the theory of planned behavior and expanded with other constructs. The factors that were examined include basic knowledge of AI, subjective norm, AI for social good, perceived self-efficacy, self-transcendent goals, personal relevance, AI anxiety, behavioral intention to learn AI, and actual learning of AI. Using a duly validated questionnaire, we surveyed 796 pre-service teachers in Nigerian Universities. Through structural equation modeling approach analyses, our proposed model explains about 79% of the variance in pre-service teachers' intention to learn AI. Basic knowledge and subjective norm were found to be the most important determinant in pre-service teachers’ intention to learn AI. All our hypotheses were supported except…
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Generative AI can assist with lesson planning, resource creation, managing professional relationships, reducing workload, and cognitive demands while enhancing teaching efficiency.
“For example, gen-AI can assist with lesson planning, resource creation, managing professional relationships, reducing workload, and cognitive demands while enhancing teaching efficiency (Mishra et al., 2023; Sanusi et al., 2024).”
From Mentorship in the Age of Generative AI: ChatGPT to Support Self-Regulated Learning of Pre-Service Teachers Before and During Placements (2025) · cites this paper as Sanusi et al., 2024
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The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587 · see citing works
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287 · see citing works
The theory of planned behavior: Frequently asked questions
Ajzen, I. (2020) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.195 · Cited by 3,347 · see citing works
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · see citing works
The role of involvement in attention and comprehension processes
Celsi, R. L., & Olson, J. C. (1988) · Journal of Consumer Research · doi:10.1086/209158 · Cited by 2,070 · see citing works
Normative social influence is underdetected
Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2008) · Personality and Social Psychology Bulletin · doi:10.1177/0146167208316691 · Cited by 1,772 · see citing works
Factors influencing teachers’ intention to use technology: Model development and test
Teo, T. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.06.008 · Cited by 914 · see citing works
Nygren, B., Aléx, L., Jonsén, E., Gustafson, Y., Norberg, A., & Lundman, B. (2005) · Aging & Mental Health · doi:10.1080/1360500114415 · Cited by 589 · see citing works
Xia, Q., Chiu, T. K. F., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104582 · Cited by 469 · see citing works
Self-citationA systematic review of teaching and learning machine learning in K-12 education
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11416-7 · Cited by 212 · see citing works
Self-citationThe role of learners’ competencies in artificial intelligence education
Sanusi, I. T., Olaleye, S. A., Agbo, F. J., & Chiu, T. K. F. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100098 · Cited by 139 · see citing works
Self-citationCo-designing AI literacy exhibits for informal learning spaces
Long, D., Blunt, T., & Magerko, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476034 · Cited by 128 · see citing works
Reeves, S. L., Henderson, M. D., Cohen, G. L., Steingut, R. R., Hirschi, Q., & Yeager, D. S. (2021) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000246 · Cited by 34 · see citing works
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Examining artificial intelligence literacy among pre-service teachers for future classrooms
Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100179 · Cited by 238 · see citing works
Self-citationMah, D.-K., & Groß, N. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00490-1 · Cited by 218 · see citing works
“Consequently, investing in the professional development of lecturers has emerged as a fundamental requirement (Eickelmann et al., 2021; Sanusi et al., 2024).”
Marzano, D. (2026) · Technology, Knowledge and Learning · doi:10.1007/s10758-025-09853-7 · Cited by 51 · see citing works
Nguyen (Ruby), N. N., & Barbieri, W. (2025) · Education Sciences · doi:10.3390/educsci15060642 · Cited by 15 · see citing works
Reina-Parrado, M., Román-Graván, P., & Hervás-Gómez, C. (2025) · European Journal of Educational Research · doi:10.12973/eu-jer.14.1.249 · Cited by 13 · see citing works
- Authors
- Sanusi, I. T., Ayanwale, M. A., & Tolorunleke, A. E.
- Affiliations
- University of Eastern Finland (FI)
- University of Johannesburg (ZA)
- Corresponding author
- Ayanwale, Musa Adekunle
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100202
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4390629445
- Cited by
- 137
- References
- 14
- Citations by year
- 2026: 46 · 2025: 70 · 2024: 21
- FWCI
- 51.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Teacher Education and Leadership Studies
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- pre-service teachers, theory of planned behavior, subjective norms, AI knowledge, self-efficacy, AI anxiety, personal relevance, AI for social good, Nigerian universities
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