The Role of Generative Artificial Intelligence in Supporting Student Learning Motivation Based on Self-Determination Theory - Desy Maryani; Ridwan Daud Mahande; Wirawan Setialaksana; Hendra Jaya; Andi Akram Nur Risal (PT. Global Research Collaboration, 2026)
Background/Context: Generative Artificial Intelligence (AI) in higher education has transformed students’ learning practices through instant access to explanations, feedback, and academic support. However, its influence on learning motivation remains contradictory. While AI may enhance autonomy and competence, excessive reliance on it may reduce cognitive engagement and self-regulation. Therefore, the use of AI needs to be examined from the perspective of students’ psychological needs. Objective/Purpose: This study aimed to examine the effects of autonomy, learning competence, AI usage competence, and relatedness on students’ learning motivation in AI-assisted learning environments based on Self-Determination Theory. Method: This study employed a quantitative correlational design using PLS-SEM. Data were collected from 248 students of the Informatics and Computer Engineering Education Programme at Universitas Negeri Makassar through a validated questionnaire. Results: The findings revealed that autonomy, learning competence, and relatedness had positive and significant effects on learning motivation, with relatedness emerging as the strongest predictor. In contrast, AI usage competence did not significantly affect learning motivation, indicating that technical proficiency in operating AI tools alone is insufficient to foster meaningful academic engagement. The structural model explained 73% of the variance in learning motivation, demonstrating strong predictive power. Conclusion: This study confirms that students’ motivation in AI-assisted learning is influenced more by the fulfilment of psychological needs than by technological capability alone. The findings extend the application of Self-Determination Theory in the context of generative AI by distinguishing between academic competence and AI usage competence. Therefore, AI integration should emphasise human-centred pedagogical strategies that strengthen autonomy, competence, and social connectedness in order to foster meaningful and sustainable learning motivation