AI-Driven Learning: Mediating and Moderating Dynamics in Self-Regulated Learning. - Mahniza, Melda; Sari, Resti Elma; Suci, Puji Hujria; Saputra, Indra; Putri, Elviza Yeni (Journal of Educational Science and Technology (EST), 2024)
The rapid integration of artificial intelligence (AI) in education has transformed how students learn, particularly in fostering self-regulated learning (SRL). However, understanding the mechanisms and conditions under which AI adoption influences SRL remains underexplored. This study investigates the roles of achievement goals, cognitive load, personalized learning, students' adaptability, and AI competence in shaping SRL within an AI-enhanced educational framework. The research employs Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach to analyze direct, mediating, and moderating effects while accounting for demographic controls such as age, gender, internet access, and environment. The findings reveal a complex interplay of factors. Direct effect testing showed that five hypothesized relationships, including the influence of achievement goals, cognitive load, personalized learning, and students’ adaptability on SRL, were unsupported. Mediation analysis confirmed that AI adoption significantly mediates the effects of achievement goals, cognitive load, and personalized learning on SRL, emphasizing the role of technology acceptance in enhancing learning autonomy. Moderation analysis identified that AI competence strengthens the relationship between achievement goals and SRL but does not moderate other interactions, such as those involving AI adoption or cognitive load. These results underscore the nuanced dynamics between cognitive, technological, and motivational factors in AI-enhanced learning. The study contributes to the growing literature on AI-driven education by highlighting the pivotal role of mediating variables like AI adoption and the limited yet strategic influence of AI competence. Future research should explore broader contextual and pedagogical factors to optimize the integration of AI tools in fostering self-regulated learning