Competence-Aware Retrieval-Augmented Generation for AI-Driven Tutoring in Software Engineering Education

Artificial Intelligence (AI) driven tutoring is increasingly used in digital and hybrid Software Engineering (SE) education to provide real-time feedback and explanations to students without the need for human tutors. However, many current approaches rely on generic Large Language Models (LLMs) that are not sufficiently tailored to the course materials and do not adapt to students' individual competence levels and preferences. As a result, the responses may lack contextual relevance and pedagogical differentiation. In this paper, we present a competence-aware Retrieval-Augmented Generation (RAG) approach for AI-driven tutoring in SE education, grounded in a requirements analysis involving 34 students. Based on the identified requirements regarding trust, task support, and adaptive feedback, we developed a system that automatically processes course materials, such as analyzing lecture slides, documents, and videos, and enriches them through summarization, semantic tagging, and cross-material linking. The processed resources serve as a knowledge base for an AI-tutor capable of answering course-specific questions and referencing course material via semantic search. The AI-tutor adapts the depth and structure of explanations to the students' competence level, motivational preferences, and assessment conditions. We implemented the AI-tutor in MEITREX, an intelligent tutoring system for SE education, and evaluated it in a user study with 17 students using a dedicated SE course environment. The results show that reference-based responses contributed positively to perceived trust and usability, while competence-aware and proactive mechanisms revealed a need for further refinement. The findings highlight design aspects for integrating competence-aware RAG into SE education.