Paper Type

ERF

Abstract

Artificial intelligence (AI) has rapidly changed learning practices in higher education. Although prior studies have examined the effects of AI on student performance, findings remain mixed and provide limited insight into students’ motivation for using AI in learning. Drawing on Self-Determination Theory (SDT), this study examines how AI use influences student motivation through the satisfaction of three psychological needs: autonomy, competence, and relatedness. Using a mixed-methods research design, the study first conducts a qualitative analysis to explore students’ experiences with AI-supported learning and identify factors shaping psychological need satisfaction. Insights from this phase inform the development of a quantitative survey used to test relationships among AI use, psychological needs, and learning motivation. By shifting attention from learning outcomes to psychological experiences, this study contributes to understanding how AI affects student engagement and provides practical guidance for integrating AI in ways that support meaningful and motivated learning in higher education.

Paper Number

1906

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Aug 15th, 12:00 AM

AI as a Learning Partner: Understanding Student Motivation Through Self-Determination Theory

Artificial intelligence (AI) has rapidly changed learning practices in higher education. Although prior studies have examined the effects of AI on student performance, findings remain mixed and provide limited insight into students’ motivation for using AI in learning. Drawing on Self-Determination Theory (SDT), this study examines how AI use influences student motivation through the satisfaction of three psychological needs: autonomy, competence, and relatedness. Using a mixed-methods research design, the study first conducts a qualitative analysis to explore students’ experiences with AI-supported learning and identify factors shaping psychological need satisfaction. Insights from this phase inform the development of a quantitative survey used to test relationships among AI use, psychological needs, and learning motivation. By shifting attention from learning outcomes to psychological experiences, this study contributes to understanding how AI affects student engagement and provides practical guidance for integrating AI in ways that support meaningful and motivated learning in higher education.