ISSN: 2226-6348
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The rapid advancement of Artificial Intelligence (AI) has transformed educational practices, creating opportunities for more personalized and effective learning experiences. This study investigates the impact of AI-driven methodologies and digital tools on learning outcomes in higher education, with a particular focus on educational institutions in Beijing. The research integrates key constructs such as perceived ease of use, perceived usefulness, and adaptive feedback, with user acceptance serving as a mediating factor influencing students’ academic performance. A quantitative design was adopted, collecting survey data from 640 respondents, including 234 teachers and 367 students. The results reveal that students perceived all three factors—ease of use, usefulness, and adaptive feedback—as significantly enhancing learning outcomes through user acceptance. However, teachers indicated that perceived usefulness did not exert a significant influence on their acceptance of AI tools. These findings underscore the importance of usability and adaptive features in shaping positive attitudes toward AI in education. The study contributes to educational management by offering insights into the integration of AI technologies, while also outlining limitations and directions for future research.
Abuhassna, H., Yahaya, N., Zakaria, M. A. Z. M., Zaid, N. M., Samah, N. A., Awae, F., Nee, C. K., & Alsharif, A. H. (2023). Trends on Using the Technology Acceptance Model (TAM) for Online Learning: A Bibliometric and Content Analysis. International Journal of Information and Education Technology, 13(1), 131–142. https://doi.org/10.18178/ijiet.2023.13.1.1788
Aldraiweesh, A. A., & Alturki, U. (2025). The Influence of Social Support Theory on AI Acceptance: Examining Educational Support and Perceived Usefulness using SEM analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3534099
Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT. Scientific Reports, 15(1), 7169. https://doi.org/10.1038/s41598-025-91634-4
Cao, L., & Chen, R. (2024). Secondary Education (High School) in China. 137–210. https://doi.org/10.1007/978-981-97-7415-9_4
Chen, X., Jiang, L., Zhou, Z., & Li, D. (2025). Impact of perceived ease of use and perceived usefulness of humanoid robots on students' intention to use. Acta Psychologica, 258, 117. https://doi.org/10.1016/j.actpsy.2025.105217
Chinnasami, S., & Manickam, R. (2023). Classification of the Architecture Using IBM SPSS Statistics. REST Publisher.https://doi.org/10.46632/aae/1/3/5
Contrino, M. F., Reyes-Millán, M., Vázquez-Villegas, P., & Membrillo-Hernández, J. (2024). Using an adaptive learning tool to improve student performance and satisfaction in online and face-to-face education for a more personalized approach. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00292-y
Delideli, J. (2024). Statistical Test Proficiency and Data Analysis Literacy through Statistical Package for the Social Sciences (SPSS) Workshop. Journal of Interdisciplinary Perspectives, 2(10), 254-264.https://doi.org/10.69569/jip.2024.0438
Dönmez, E. (2024). The future of education: AI-supported reforms in the USA and China. In Global Agendas and Education Reforms: A Comparative Study (pp. 135-150). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-3068-1_7
Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022). How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178(178), 121598. https://doi.org/10.1016/j.techfore.2022.121598
Gan, Z., An, Z., & Liu, F. (2021). Teacher feedback practices, student feedback motivation, and feedback behavior: How are they associated with learning outcomes? Frontiers in Psychology, 12(1). https://doi.org/10.3389/fpsyg.2021.697045
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda?. Technological Forecasting and Social Change, 162, 120392.https://doi.org/10.1016/j.techfore.2020.120392
Jeilani, A., & Abubakar, S. (2025, March). Perceived institutional support and its effects on student perceptions of AI learning in higher education: the role of mediating perceived learning outcomes and moderating technology self-efficacy. In Frontiers in Education (Vol. 10, p. 98). Frontiers Media SA. https://doi.org/10.3389/feduc.2025.1548900
Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19. https://doi.org/10.1108/IJILT-05-2020-0090
Li, Y. (2023). Where does AI-driven Education, in the Chinese Context and Beyond, go next? International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00341-6
Linus, A. A., Aladesusi, G. A., Monsur, I. A., & Elizabeth, F. J. (2025). Perceived Usefulness, Ease of Use, And Intention to Utilize Online Tools for Learning Among College of Education Students. http://ejournal.upi.edu/index.php/
Liu, H.-L., Wang, T.-H., Lin, H.-C. K., Lai, C.-F., & Huang, Y.-M. (2022). The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.858411
Marikyan, D., & Papagiannidis, S. (2024). Technology Acceptance Model: A Review. Open.ncl.ac.uk. https://open.ncl.ac.uk/theories/1/technology-acceptance-model/
Mathias Mejeh, Sarbach, L., & Hascher, T. (2024). Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learning. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12510-8
Mejeh, M., Sarbach, L., & Hascher, T. (2024). Effects of adaptive feedback through a digital tool–a mixed-methods study on the course of self-regulated learning. Education and Information Technologies, 29(14), 1-43.https://link.springer.com/article/10.1007/s10639-024-12510-8
Nuryakin , Pierre, L., & Hussein Gibreel Musa. (2023). The Effect of Perceived Usefulness and Perceived Easy to Use on Student Satisfaction The Mediating Role of Attitude to Use Online Learning. APMBA (Asia Pacific Management and Business Application), 011(03), 323–336. https://doi.org/10.21776/ub.apmba.2023.011.03.5
Puspitasari, A., & Nugraha, H. (2023). The impact of perceived ease of use and perceived usefulness on actual system use through intention to use as an intervening variable in subscription video on demand services (Study on Netflix users in Semarang City). World Journal of Advanced Research and Reviews, 2023(03), 1354–1366. https://doi.org/10.30574/wjarr.2023.18.3.1230
Rafiq, S., Iqbal, S., & Afzal, A. (2024). The impact of digital tools and online learning platforms on higher education learning outcomes. Al-Mahdi research journal (MRJ), 5(4), 359-369.http://ojs.mrj.com.pk/index.php/MRJ/article/view/342
Saad, S., Ramli, Z., Sarmila, M. S., Nor, M., & Ali, S. (2025). Exploring the Adoption of AI-Driven Adaptive Learning in Higher Education: A Multidimensional TAM Perspective. International Journal of Academic Research in Business and Social Sciences, 15(5), 1011-1025.https://kwpublications.com/papers_submitted/17068/exploring-the-adoption-of-ai-driven-adaptive-learning-in-higher-education-a-multidimensional-tam-perspective.pdf
Shaikh, I. M., Tanakinjal, G. H., Amin, H., Noordin, K., & Shaikh, J. (2025). Students’e-learning acceptance: empirical evidence from higher learning institutions. On the Horizon: The International Journal of Learning Futures, 33(1), 1-13.https://www.emerald.com/oth/article-abstract/33/1/1/1239888
Sukmawati, S. (2023). Development of quality instruments and data collection techniques. Jurnal Pendidikan Dan Pengajaran Guru Sekolah Dasar (JPPGuseda), 6(1), 119-124.https://journal.unpak.ac.id/index.php/JPPGuseda/article/view/7527
Taherdoost, H. (2021). Data collection methods and tools for research; a step-by-step guide to choose data collection technique for academic and business research projects. International Journal of Academic Research in Management (IJARM), 10(1), 10-38.https://hal.science/Hal-03741847/
Valaboju, V. K. (2024). The Synergy of Just-in-Time Learning and Artificial Intelligence: Revolutionizing Personalized Education. International Journal of Computer Engineering and Technology (IJCET), 15(5), 707-715.https://doi.org/10.5281/zenodo.13884988
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational technology & society, 24(3), 116-129. http://index.j-ets.net/Published/24_3/ETS_24_3_09.pdf
Xu, Z. (2024). AI in education: Enhancing learning experiences and student outcomes. Applied and Computational Engineering, 51(1), 104–111. https://doi.org/10.54254/2755-2721/51/20241187
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser?Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00420-7
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser?Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00420-7
Wang, Y., & Nasri, N. B. M. (2025). Revolutionizing Learning Outcomes: The Integration of Educational Management, Ai-Driven Methodologies, and Digital Tools. International Journal of Academic Research in Progressive Education and Development, 14(4), 2299–2314.
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