As artificial intelligence (AI) continues to reshape educational practices, higher education institutions are incorporating AI tools to modernize instructional techniques and boost learner success. This paradigm shift has highlighted the importance of engagement and psychological well-being (PWB) in academic research, addressing the challenges learners encounter in AI-enhanced environments. Both external factors, like teacher support, and internal factors, such as motivation, play crucial roles in the learning process. Guided by Self-determination theory (SDT), this study seeks to explore the influence of teacher support on promoting learner engagement and PWB, considering the possible mediating effects of learners’ motivation in AI-based contexts. For this purpose, data were gathered from 442 international overseas students across various academic disciplines in Chinese colleges, utilizing Structural Equation Modeling (SEM) to assess the measurement model and conducting multiple regression analyses. The results indicate that perceived teacher support positively predicts both engagement and PWB, with motivation serving as a significant mediating variable. This finding suggests that motivation acts as a psychological bridge between external instructional support and internal learning experiences in AI contexts. Moreover, AI-driven features—such as personalized prompts and adaptive feedback—may amplify or complement traditional sources of support, offering new pathways for student empowerment. This study contributes to the literature by contextualizing SDT within AI-mediated education and demonstrating how hybrid support systems—human and algorithmic—shape learner outcomes. Implications for instructional design and teacher training are discussed, including the need to align emotional support strategies with AI analytics to personalize learning and foster positive academic experiences.
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