A counterfactual explainable AI-driven approach to support students’ self-regulated learning in computer-based learning environments

In this study, we propose a counterfactual explainable AI-driven self-regulated learning (SRL) training system for computer-based learning environments. The system uses real-time log data to generate an AI-predicted final test score and provides a personalized, interactive study guide that helps students reflect on their current SRL engagement and plan how to allocate time across SRL-relevant strategies. Students can set a desired final test score and explore "what-if" scenarios through counterfactual recommendations (e.g., specific changes to their learning strategies needed to reach the desired score) generated using Diverse Counterfactual Explanations (DiCE). We will evaluate the system in a between-subjects randomized controlled trial with 190 participants. We will examine whether receiving the counterfactual-driven SRL training improves learning gains and metacognitive calibration, and increases engagement in metacognitive strategies.