Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system
This paper introduces the design and development of a framework that integrates a large language model (LLM) with a retrieval-augmented generation (RAG) approach leveraging both a knowledge graph and user interaction history. The framework is incorporated into a previously developed adaptive learning support system to assess learners' code, generate formative feedback, and recommend exercises. Moerover, this study examines learner preferences across three instructional modes; adaptive, Generative AI (GenAI), and hybrid GenAI-adaptive. An experimental study was conducted to compare the learning performance and perception of the learners, and the effectiveness of these three modes using fou...
| Authors | Lalita Na Nongkhai, Jingyun Wang, Adam Wynn et al. |
|---|---|
| Primary category | cs.PL |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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