THE EFFECT OF ADAPTIVE AI SYSTEMS ON STUDENT MOTIVATION AND PERFORMANCE IN HIGHER EDUCATION
Keywords:
Artificial intelligence, adaptive learning systems, motivation, academic performance, higher education, digital transformation, personalized learning.Abstract
This article explores the impact of adaptive artificial intelligence (AI) systems on student motivation and academic performance in higher education. As digital transformation accelerates in university environments, AI-driven learning platforms are increasingly used to tailor educational experiences to individual learner needs. Drawing on a combination of theoretical sources and empirical observations, the study analyzes how adaptive systems support personalized learning paths, formative feedback, and learner autonomy. The findings suggest that such technologies can significantly enhance student engagement and outcomes when properly implemented and supported by pedagogical and institutional frameworks.
References
Chou, C. Y., & Chan, T. W. (2021). Designing intelligent tutoring systems for enhancing learning engagement. Educational Technology & Society, 24(3), 45–57.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.
Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Khosravi, H., Kitto, K., & Lambert, C. (2022). AI and Learning Analytics in Higher Education: A Systematic Review. British Journal of Educational Technology, 53(2), 371–389.
Martin, F., Sunley, R., & Turner, R. (2019). Gamification and student engagement: An empirical study of the influence of gamified learning. Journal of Computer Assisted Learning, 35(6), 782–793.
OECD. (2020). Digital Education Outlook: Pushing the Frontiers with AI, Blockchain and Robots. OECD Publishing. https://doi.org/10.1787/9789264379050-en
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2017). Informing Progress: Insights on Personalized Learning Implementation and Effects. RAND Corporation.
Xu, B., & du Boulay, B. (2020). Intelligent Tutoring Systems and Motivation: A Review of the Literature. International Journal of Artificial Intelligence in Education, 30(1), 74–95.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on AI applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27.
Downloads
Issue
Section
License
New knowledge Journal of science by Univesity of agribusiness and rural development is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://science.uard.bg/index.php/newknowledge/issue/archive.
Permissions beyond the scope of this license may be available at http://science.uard.bg/index.php/newknowledge/index.
