Who Thrives with AI? A Moderation Analysis of the Impact of AI-Based Learning on Students’ Subjective Wellbeing by Learning Style

This study investigates the impact of AI-based instruction on students’ subjective well-being and whether individual learning styles moderate this impact. Grounded in Self-Determination Theory (SDT)—which emphasizes the basic psychological needs of autonomy, competence, and relatedness—and the VARK framework (Visual, Auditory, Read/Write, Kinesthetic), the research explores how AI-enhanced environments support wellbeing through learner-centered personalization. An experimental design was implemented with 465 high school students assigned to either AI-based instruction or traditional teaching methods. Subjective well-being was measured using a validated multidimensional scale aligned with SDT constructs. Moderation analysis revealed that while AI-based instruction significantly enhanced overall student well-being, the magnitude of the effect varied by learning style. Visual, Read/Write, Kinesthetic, and Multimodal learners reported higher well-being in the AI-based condition, whereas Auditory learners showed no statistically significant benefit. Kinesthetic and Multimodal learners experienced the most tremendous improvement, particularly in perceived competence and autonomy. These findings suggest that AI-based learning environments can promote student well-being when designed to fulfill basic psychological needs and align with individual learning preferences. The integration of SDT and VARK offers a novel framework for developing adaptive, human-centered AI systems that foster engagement and psychological well-being in educational settings.