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<p>This study investigates how AI-enhanced learning environments contribute to student well-being and autonomy, emphasizing the roles of teacher-student interactions, social-emotional learning (SEL), and personalized learning pathways. Drawing on survey data from a diverse group of university students, the study employed Growth Mixture Modeling (GMM) and Latent Profile Analysis (LPA) to uncover distinct patterns of well-being and engagement within AI-supported educational contexts. The findings revealed multiple developmental trajectories, with some students demonstrating high emotional resilience and motivation in response to personalized and supportive learning conditions, while others showed signs of disengagement and required targeted support. The analysis also identified qualitatively distinct learner profiles, reflecting varied experiences with well-being and engagement. Overall, the results underscore the importance of teacher support and adaptive learning systems in fostering autonomy and emotional stability. The study calls for more responsive, AI-driven educational strategies that can meet diverse learner needs and promote holistic development.</p>

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Interaction, SEL, Autonomy, and Personalization as Predictors of Student Well-Being in AI Contexts: A Growth Mixture Modeling Study

  • 期刊论文

  • 发表日期：

    2026年3月8日

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  • Kaifang Deng,
  • Zimo Ouyang

摘要

This study investigates how AI-enhanced learning environments contribute to student well-being and autonomy, emphasizing the roles of teacher-student interactions, social-emotional learning (SEL), and personalized learning pathways. Drawing on survey data from a diverse group of university students, the study employed Growth Mixture Modeling (GMM) and Latent Profile Analysis (LPA) to uncover distinct patterns of well-being and engagement within AI-supported educational contexts. The findings revealed multiple developmental trajectories, with some students demonstrating high emotional resilience and motivation in response to personalized and supportive learning conditions, while others showed signs of disengagement and required targeted support. The analysis also identified qualitatively distinct learner profiles, reflecting varied experiences with well-being and engagement. Overall, the results underscore the importance of teacher support and adaptive learning systems in fostering autonomy and emotional stability. The study calls for more responsive, AI-driven educational strategies that can meet diverse learner needs and promote holistic development.