How Are Self-Determination Theory Needs Satisfaction and Personality Traits Associated with Self-Regulated Learning Using Generative Artificial Intelligence?
Year: 2026
Type: article
Study design: Observational Study
Abstract: Personality traits and self-determination theory (SDT) needs support represent individual and contextual factors associated with the three phases of self-regulated learning (SRL) with generative artificial intelligence (GenAI), namely forethought, performance, and self-reflection. This study examines the relationships between the Big Five personality traits and each SRL phase and whether SDT needs satisfaction from GenAI moderates these relationships. Participants were 400 university students who completed validated questionnaires. Our analysis revealed that openness, conscientiousness, and neuroticism were significantly associated with forethought; openness and conscientiousness were significantly associated with performance; and openness was significantly associated with self-reflection. We also found that SDT needs satisfaction significantly moderated only the relationship between extraversion and forethought. We also found that only the interaction between extraversion and SDT needs satisfaction was statistically significant in the forethought model. Further analysis indicated a crossover pattern in the estimated association between extraversion and forethou]ght across levels of SDT needs satisfaction; however, neither conditional association reached statistical significance. No statistically significant interaction was observed for performance or self-reflection. In the present sample, personality traits showed more consistent associations with the SRL phases than the observed interaction effects involving SDT needs satisfaction. Theoretically, the findings indicate that individual differences and situational motivational support showed different patterns of association with SRL in the GenAI-supported learning context. Practically, educators may consider students’ diverse personality profiles when designing GenAI-supported learning activities.
Authors: Qi Xia, Runjie Jiang, Zili Zhang, Thomas K.F. Chiu
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7204128107