Preservice Teachers’ Perceptions of AI-Powered Adaptive Learning Models
DOI:
https://doi.org/10.62951/icgel.v2i2.187Keywords:
Adaptive Learning, Artificial Intelligence, Higher Education, Learning Perception, Preservice TeachersAbstract
The integration of Artificial Intelligence (AI) in higher education has led to the increasing use of AI-powered adaptive learning models that support personalized and data-driven learning. However, studies examining preservice teachers’ perceptions of these models remain limited, despite their important role in future classroom implementation. This study aims to explore preservice teachers’ perceptions of AI-powered adaptive learning in higher education, focusing on perceived usefulness, learning adaptivity, learning experience, and perceived concerns. A descriptive qualitative research design was employed involving 53 preservice teachers from various universities. Data were collected using a Likert-scale questionnaire and open-ended questions. Quantitative data were analyzed descriptively using percentage distributions, while qualitative data were examined through simple thematic analysis. The findings reveal that preservice teachers generally demonstrate positive perceptions of AIpowered adaptive learning, particularly in terms of learning effectiveness, adaptability, and engagement. Nevertheless, concerns related to over-reliance on AI, ethical issues, and data privacy were also identified. These results indicate that preservice teachers show readiness to engage with AI-supported learning, while highlighting the need for teacher education programs to promote responsible and pedagogically informed AI integration.
References
Chiu, T. K., & Chai, C. S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568
Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Mustafaoğlu, R. (2025). Artificial intelligence literacy in teacher education: Pre-service teachers’ perceptions and readiness. Frontiers in Education, 10, 1–12. https://doi.org/10.3389/feduc.2025.1456732
Nasrudin, N. S., & Hashim, H. (2025). Artificial intelligence applications in education: Benefits, challenges, and teacher readiness. Education and Information Technologies, 30(4), 6079–6095. https://doi.org/10.1007/s10639-025-13328-7
OECD. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. OECD Publishing.
Oh, Y. J., & Ahn, J. (2024). Teachers’ ethical concerns and perceptions toward artificial intelligence in education. Computers and Education: Artificial Intelligence, 5, 1–17. https://doi.org/10.1016/j.caeai.2024.100145
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
Qi, K. (2025). Teacher perceptions and readiness for integrating AI-powered learning platforms into classroom practice. Uniglobal Journal of Social Sciences and Humanities, 3(2), 160–170.
Sibarani, D. P., Cendana, W., & Anggraeni, C. K. P. (2025). Teachers’ perceptions and readiness for AI-based virtual assistants in supporting schools’ learning transformation. Journal of Practice Learning and Educational Development, 5(2), 215–226. https://doi.org/10.58737/jpled.v5i1.398
Sivanganam, J., Yunus, M. M., & Said, N. E. M. (2025). Teachers’ perceptions of using artificial intelligence in ESL classrooms. International Journal of Academic Research in Progressive Education and Development, 14(1), 2408–2424. https://doi.org/10.6007/IJARPED/v14-i1/25034
Teo, T. (2020). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000376709
Varma, R. R., Umesh, I. M., Nagesh, Y. N., & Kumara Swamy, K. S. (2021). Digital transformation in higher education institutions: An overview. International Journal of Applied Engineering Research, 16(4), 278–282.
Wang, X., Xu, X., Zhang, Y., Hao, S., & Jie, W. (2024). Exploring the impact of artificial intelligence application in personalized learning environments: Thematic analysis of undergraduates’ perceptions in China. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-024-04168-x
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhai, X., He, P., & Krajcik, J. (2021). Applying machine learning in science assessment: A systematic review. Computers & Education, 174, 104305. https://doi.org/10.1016/j.compedu.2021.104305
