Owlgorithm: Supporting Self-Regulated Learning in Competitive Programming through LLM-Driven Reflection

• URL: http://arxiv.org/abs/2511.09969v1
• Date: Fri, 14 Nov 2025 01:22:55 GMT
• Title: Owlgorithm: Supporting Self-Regulated Learning in Competitive Programming through LLM-Driven Reflection
• Authors: Juliana Nieto-Cardenas, Erin Joy Kramer, Peter Kurto, Ethan Dickey, Andres Bejarano,
• Abstract summary: We present an educational platform that supports Self-Regulated Learning (SRL) in competitive programming (CP)<n>Owlgorithm produces context-aware, meta prompts tailored to individual student submissions.<n>Our exploratory assessment of student ratings and TA feedback revealed both promising benefits and notable limitations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Owlgorithm, an educational platform that supports Self-Regulated Learning (SRL) in competitive programming (CP) through AI-generated reflective questions. Leveraging GPT-4o, Owlgorithm produces context-aware, metacognitive prompts tailored to individual student submissions. Integrated into a second- and third-year CP course, the system-provided reflective prompts adapted to student outcomes: guiding deeper conceptual insight for correct solutions and structured debugging for partial or failed ones. Our exploratory assessment of student ratings and TA feedback revealed both promising benefits and notable limitations. While many found the generated questions useful for reflection and debugging, concerns were raised about feedback accuracy and classroom usability. These results suggest advantages of LLM-supported reflection for novice programmers, though refinements are needed to ensure reliability and pedagogical value for advanced learners. From our experience, several key insights emerged: GenAI can effectively support structured reflection, but careful prompt design, dynamic adaptation, and usability improvements are critical to realizing their potential in education. We offer specific recommendations for educators using similar tools and outline next steps to enhance Owlgorithm's educational impact. The underlying framework may also generalize to other reflective learning contexts.

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