Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system
arXiv cs.AI / 3/27/2026
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Key Points
- The paper presents a framework that combines an LLM with retrieval-augmented generation using both a knowledge graph and learners’ interaction history to provide formative code feedback and exercise recommendations.
- The framework is embedded in an existing adaptive programming learning system and evaluated across three instructional modes: adaptive-only, GenAI-only, and a hybrid GenAI-adaptive approach.
- Using data from 4 log features derived from 4,956 code submissions, results show GenAI-based modes produce significantly more correct code and fewer submissions missing essential programming logic than adaptive-only feedback.
- The hybrid GenAI-adaptive mode performs best overall, delivering the highest number of correct submissions and the fewest incorrect or incomplete attempts compared with either single-mode approach.
- Survey results indicate learners generally find GenAI-generated feedback helpful, and all modes are rated positively for perceived ease of use and usefulness.
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