Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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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. Moreover, 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...

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