An AI‐Supported Pedagogical Architecture to Foster Self‐Regulated Learning in Virtual Environments

ABSTRACT This study reports on the design, implementation, and evaluation of an AI‐Supported Pedagogical Architecture (PA) aimed at fostering Self‐Regulated Learning (SRL) within a Virtual Learning Environment (VLE). The architecture was integrated into the Moodle platform and applied in an introductory Python course. It incorporates technological tools such as Completion Progress, Analytics Graphs, Athena, and the Time Tracker SRL plugin to deliver real‐time feedback, facilitate progress monitoring, and support learners in goal setting, planning, and reflection–key dimensions of SRL. Through Educational Data Mining techniques, the study identifies distinct learner profiles based on interaction logs, revealing a significant correlation between SRL strategies and academic achievement. Additionally, qualitative data from focus groups underscore the architecture's effectiveness in enhancing metacognitive awareness, student engagement, and overall satisfaction, demonstrating its potential to support the development of learner autonomy in online education.