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 AIenhanced
environments support wellbeing through learnercentered
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.

Conference Title

International Conference on Smart Learning Courses (SCME)

Conference Country

Palestine

Conference Date

July 9, 2025 - July 11, 2025

Conference Sponsor

IEEE