THE IMPACT OF ADAPTIVE LEARNING SYSTEMS ON LEARNER MOTIVATION AND ENGAGEMENT

Nigina Davronova Akhmedjon qizi

Authors

Nigina Davronova Akhmedjon qizi Yangi Asr University English language teacher

Keywords:

Keywords: Personalisation, Motivation, Learning, Adaptability, and Engagement

Abstract

Abstract. Adaptive learning systems (ALS) powered by artificial intelligence (AI) have become revolutionary tools in education, offering individualised instruction based on learner profiles. Using well-known educational psychology theories like Self-Determination Theory, Flow Theory, and Cognitive Load Theory, this theoretical article investigates the conceptual underpinnings connecting ALS with student motivation and engagement. In order to demonstrate how AI-enabled personalisation, feedback, and adaptive scaffolding can promote intrinsic motivation, long-term engagement, and better learning results, the article offers a conceptual model. Future research, educational policy, and instructional design implications are examined.

References

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

Walkington, C. (2013). Using adaptive learning technologies to personalize instruction to student interests. Journal of Educational Psychology, 105(4), 932–945.

Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. The Adaptive Web, 3–53.

D’Mello, S., & Graesser, A. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. Sottilare et al. (Eds.), Design Recommendations for Intelligent Tutoring Systems.

Knewton. (2020). Adaptive learning platform case studies. Knewton Research Report