Generative AI and student motivation: an SDT-guided systematic review of learning activities, motivational regulation, and measurement

The application of Generative AI is reshaping students’ learning. However, student motivation in GenAI-supported learning is complex and influenced by multiple factors. To fully realize the educational potential of GenAI, a comprehensive understanding of the relationship between student motivation and GenAI is necessary. To address this gap, this study conducted a systematic literature review. From the perspective of Self-Determination Theory (SDT), this review examined 39 articles published between 2023 and 2025, drawn from two databases: Web of Science and Scopus. This study provides three major findings. First, competence-related learning activities were the most frequently identified across the reviewed studies. Second, four categories were reported in relation to student motivation: GenAI characteristics, learner factors, psychological factors, and environmental factors. Third, three major aspects of motivational conceptualization are basic psychological needs, motivation construct, and motivation-related construct. In addition, quantitative questionnaires were the dominant approach used to measure these constructs. Finally, the relationship among these three findings is illustrated. The findings provide insights into student motivation in GenAI-supported learning and highlight the relevance of GenAI characteristics, learners, and the learning environment. This study also provides theoretical and practical implications for facilitating students’ motivation in GenAI-supported learning environments.