Integrating technology and self-regulation strategies to enhance learning outcomes: a dual-analysis approach

This study explores the role of technology integration in enhancing self-regulated learning (SRL) and managing cognitive load within education that involves programming content. Through a mixed-methods approach, we examined the impact of AI-enhanced online mentoring platforms, drawing on both performance data and qualitative feedback. Findings indicate that adaptive, technology-supported learning environments foster greater learner autonomy, sustained engagement, and more effective self-monitoring. Students highlighted the benefits of real-time feedback, iterative learning support, and personalized guidance in navigating complex programming tasks. Qualitative insights further revealed the development of metacognitive strategies and motivation linked to AI-driven instructional design. This research underscores the value of aligning educational technology with evidence-based pedagogical practices to support cognitive efficiency and meaningful learning experiences. It offers practical direction for designing scalable, student-centered interventions and calls for further exploration of AI-powered learning tools in diverse programming and computational education contexts.d.