Differentiated impacts of GenAI-supported self-regulated game-based science learning: a behavioural and epistemic network analysis of high- and low-achieving students

This research explores the differentiated impacts of Generative Artificial Intelligence (GenAI) integration into digital game-based learning (DGBL) environments, specifically focusing on self-regulated learning (SRL) for junior high school students with varying academic achievements. The study investigates how GenAI-supported self-regulated DGBL influences learning motivation, behavioral patterns, perceptions, and prompt usage within a physics context.

The integration of advanced AI, such as GenAI, into educational technologies represents a significant evolution in personalized learning approaches, offering scalable methods to enhance student engagement and outcomes. Understanding its differentiated impacts across various student demographics is crucial for informing future educational technology development and policy, ensuring equitable and effective deployment.

GenAI integration in digital game-based learning offers new opportunities for enhancing student learning through personalized support, immediate feedback, and adaptive interaction.

Forward consideration, not a verified fact.