Abstract

Artificial Intelligence (AI) is transforming educational landscapes by personalizing learning experiences, yet its impact on student motivation remains underexplored. This technical paper synthesizes recent research on how AI realizes key motivational frameworks: Self-Determination Theory (SDT), Expectancy-Value Theory (EVT), and Cognitive Load Theory (CLT). Drawing on empirical studies, we examine AI’s role in fulfilling psychological needs (autonomy, competence, relatedness in SDT), enhancing expectancy and value beliefs (EVT), and managing cognitive loads (intrinsic, extraneous, germane in CLT) to boost intrinsic motivation, engagement, and persistence. Findings indicate that AI-driven tools, such as chatbots and adaptive systems, can optimize motivation when designed with theoretical principles, but risks like cognitive offloading (over-reliance) and expertise mismatches must be addressed. Implications for educators, AI developers, and policy include tailored interventions for diverse learners, emphasizing inclusion and equity. This review highlights the need for integrated theoretical approaches in AI-enhanced education to sustain long-term motivational outcomes.

Keywords: Artificial intelligence, student motivation, personalized learning, self-determination theory, expectancy-value theory, cognitive load theory, educational technology