My Code Weapon: Adaptive Problem Recommendation and Knowledge Retention Scheduling in AI-assisted Programming Education

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Abstract
Learning competitive programming and advanced data structures presents a significant challenge for students due to the high cognitive load and the need for long-term concept retention. Traditional online judge platforms provide a static, one-size-fits-all experience that fails to adapt to individual student skill levels or address the exploration-exploitation tradeoff inherent in learning. In this paper, we introduce MyCodeWeapon, a novel learning platform that provides a personalized and adaptive educational environment for competitive programming. MyCodeWeapon integrates three algorithmic components: (1) a dynamic difficulty calibration model using an Elo rating system with an adaptive K-factor to jointly estimate student ability and problem difficulty; (2) a contextual multi-armed bandit based on Thompson Sampling for adaptive problem recommendation that balances exploration of new topics with exploitation of existing skills; and (3) a spaced repetition scheduler inspired by the FSRS algorithm to promote long-term concept retention. We describe the architecture of the MyCodeWeapon platform---built on Next.js, Supabase, and Judge0---and report a pilot study showing that the adaptive group achieved higher post-test scores, attempted more problems, and demonstrated greater learning efficiency. This work aims to demonstrate a more effective and engaging paradigm for scaling programming education.
Funding Information
  • Singapore Ministry of Education Academic Research Fund (MOE-T2EP20224-0009) (MOE-T2EP20224-0009)
  • NTU Startup Fund (-)

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