[2603.24940] Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system

Computer Science > Programming Languages arXiv:2603.24940 (cs) [Submitted on 26 Mar 2026] Title:Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system Authors:Lalita Na Nongkhai, Jingyun Wang, Adam Wynn, Takahiko Mendori View a PDF of the paper titled Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system, by Lalita Na Nongkhai and 3 other authors View PDF Abstract: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 four key log features derived from 4956 code submissions across all experimental groups. The analysis results show that learners receiving feedback from GenAI modes had significantly more correct code and fewer code submissions missing essential programming logic than those rece...