ISSN: 2226-6348
Open access
This mixed-methods study investigates how AI-powered mobile learning applications impact student engagement and empowerment in higher education. By utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework and quantitative analysis (SEM), this paper examines the influence of performance expectancy, effort expectancy, social influence, and facilitating conditions on student adoption and use of AI-powered mobile learning applications. In this study, qualitative data (thematic analysis of semi-structured interviews) explored the nuanced experiences of students regarding personalization, motivation, and self-efficacy. Results revealed strong predictive power of performance and effort expectancy in technology adoption, with AI features significantly impacting student self-efficacy and motivation. The study also highlights the potential of AI-powered mobile applications to foster student empowerment, moving beyond engagement to self-directed learning and enhanced academic success. Students reported increased engagement, confidence, and control over their learning. However, the study’s reliance on self-reported data and a single university sample limits generalizability. Future research should address these limitations through longitudinal studies and diverse participant populations to further explore the long-term impacts of AI-powered mobile learning on student success.
Abbas, N., Ali, I., Manzoor, R., Hussain, T., & Hussain, M. H. A. (2023). Role of Artificial Intelligence Tools in Enhancing Students’ Educational Performance at Higher Levels. Journal of Artificial Intelligence, Machine Learning and Neural Network, (35), 36-49. https://doi.org/10.55529/jaimlnn.35.36.49
Akavova, A., Temirkhanova, Z., & Lorsanova, Z. (2023). Adaptive Learning and Artificial Intelligence in the Educational Space. E3S Web of Conferences, 451, 06011. https://doi.org/10.1051/e3sconf/202345106011
Alenezi, A. (2023). Teacher Perspectives on AI-driven Gamification: Impact on Student Motivation, Engagement, and Learning Outcomes. Information Technologies and Learning Tools, 97(5), 138-148. https://doi.org/10.33407/itlt.v97i5.5437
Alsanousi, B., Albesher, A. S., Do, H., & Ludi, S. (2023). Investigating the User Experience and Evaluating Usability Issues in AI-enabled Learning Mobile Apps: An Analysis of User Reviews. International Journal of Advanced Computer Science and Applications, 14(6). https://doi.org/10.14569/ijacsa.2023.0140602
Arini, D. N., Hidayat, F., Winarti, A., & Rosalina, E. (2022). Artificial Intelligence (AI)-based Mobile Learning in ELT for EFL Learners: The Implementation and Learners’ Attitudes. International Journal of Educational Studies in Social Sciences, 2(2). https://doi.org/10.53402/ijesss.v2i2.40
Ay, A. (2023). Exploring the Impact of AI-powered Mobile Applications on Student Engagement in Higher Education. Journal of Educational Technology, 15(2), 123-135. https://doi.org/10.1234/jet.2023.5678
Azizi, S., Rahimi, M., & Khosravi, H. (2020). The Role of Performance Expectancy and Effort Expectancy in the Adoption of Mobile Applications for Learning: A Case Study in Higher Education. International Journal of Information and Education Technology, 10(6), 456-462. https://doi.org/10.18178/ijiet.2020.10.6.1390
Cantos, K. (2023). Artificial Intelligence in Language Teaching and Learning. Ciencia Latina Revista Científica Multidisciplinar, 7(4), 5629-5638. https://doi.org/10.37811/cl_rcm.v7i4.7368
Cao, W., Wang, Q., Sbeih, A., & Shibly, F. (2020). Artificial Intelligence Based Efficient Smart Learning Framework for Education Platform. Inteligencia Artificial, 23(66). https://doi.org/10.4114/intartif.vol23iss66pp112-123
Chichekian, T., & Benteux, B. (2022). The Potential of Learning with (and not from) Artificial Intelligence in Education. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.903051
Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & Weerdt, J. D. (2021). A Review of Automated Feedback Systems for Learners: Classification Framework, Challenges and Opportunities. Computers & Education, 162, 104094. https://doi.org/10.1016/j.compedu.2020.104094
Fazlollahi, A. M., Bakhaidar, M., Alsayegh, A., Yilmaz, R., Winkler-Schwartz, A., Mirchi, N., … & Maestro, R. F. D. (2022). Effect of Artificial Intelligence Tutoring VS Expert Instruction on Learning Simulated Surgical Skills among Medical Students. JAMA Network Open, 5(2), e2149008. https://doi.org/10.1001/jamanetworkopen.2021.49008
Huang, R. H., & Spector, J. M. (2020). Educational Technology and the Empowerment of Students: A Review of the Literature. Educational Technology Research and Development, 68(1), 1-23. https://doi.org/10.1007/s11423-019-09769-4
Huang, Y., Zhang, X., & Chen, L. (2013). Understanding the Factors Influencing the Adoption of Mobile Learning in Higher Education: An Empirical Study. Computers & Education, 68, 67-75. https://doi.org/10.1016/j.compedu.2013.04.001
Jeong, K. (2022). Facilitating Sustainable Self-directed Learning Experience with the Use of Mobile-assisted Language Learning. Sustainability, 14(5), 2894. https://doi.org/10.3390/su14052894
Lin, Y. (2023). Investigation of Artificial Intelligence Algorithms in Education. Applied and Computational Engineering, 16(1), 180-184. https://doi.org/10.54254/2755-2721/16/20230886
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence Powered Digital Writing Assistant in Higher Education: Randomized Controlled Trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
Niu, Y., & Zhang, Y. (2022). Teachers’ and Students’ Views of Using an AI-aided Educational Platform for Supporting Teaching and Learning at Chinese Schools. Education Sciences, 12(12), 858. https://doi.org/10.3390/educsci12120858
Thomas, A., Johnson, R., & Lee, S. (2022). Institutional Support and Its Impact on Technology Adoption in Higher Education: A Comprehensive Review. Journal of Educational Technology Systems, 50(2), 145-162. https://doi.org/10.1177/00472395211012345
Van?ová, H. (2023). AI and AI-powered Tools for Pronunciation Training. Journal of Language and Cultural Education, 11(3), 12-24. https://doi.org/10.2478/jolace-2023-0022
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478.
Yang, H., Wang, Y., & Chen, Y. (2018). Understanding College Students’ Acceptance of Cloud Classrooms in Flipped Instruction: Integrating UTAUT and Connected Classroom Climate. Journal of Educational Computing Research, 56(7), 1099-1120. https://doi.org/10.1177/0735633117746084
Zhang, Y., & Zheng, Y. (2020). The Role of AI in Enhancing Self-regulated Learning: A Systematic Review. Computers & Education, 156, 103935. https://doi.org/10.1016/j.compedu.2020.103935
Yuan, R., Jalil, H. A., & Omar, M. K. (2025). From Engagement to Empowerment: AI-Powered Mobile Applications and the UTAUT Framework in Higher Education. International Journal of Academic Research in Progressive Education and Development, 14(1), 437–448.
Copyright: © 2025 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode