How AI-Assisted Learning Satisfies Students' Psychological Needs: Evidence from Taiwan Universities - Chen, Chiao-Chieh; Chiu, Yu-Ping (Journal of Social and Scientific Education, 2026)
Despite widespread AI adoption in universities globally, current implementations may inadvertently undermine learning engagement, with students reporting AI use primarily due to external pressures rather than genuine interest. This disconnect between technological sophistication and psychological understanding poses immediate challenges for educational institutions seeking to maximize AI benefits while supporting student wellbeing. This study examines how AI-assisted learning influences students' basic psychological needs satisfaction through Self-Determination Theory in Taiwanese university contexts, providing the first systematic investigation of all three psychological needs (competence, autonomy, relatedness) simultaneously within AI learning environments. A cross-sectional survey of 200 Taiwanese university students was conducted using structural equation modeling, with validated scales measuring AI-assisted learning usage and basic psychological needs satisfaction (competence, autonomy, relatedness). AI-assisted learning significantly influenced all three psychological needs, with competence showing the strongest effect, followed by autonomy and relatedness. Competence need satisfaction emerged as the primary factor in AI learning contexts, consistent with findings in Chinese educational settings. This research provides the first systematic application of Self-Determination Theory to AI education contexts, demonstrating that psychological need satisfaction determines AI learning effectiveness. Competence need primacy in AI contexts challenges traditional SDT applications where autonomy typically receives primary emphasis, suggesting that technological complexity and cultural factors moderate psychological need dynamics. The findings offer evidence-based guidance for designing culturally-informed AI educational systems that prioritize competence-centered features, integrate autonomy-supporting elements, and adopt hybrid implementation models combining AI's strengths with essential human instruction elements for emotional support and interpersonal connection.