SageJavon: A scalable AI tutor for personalized programming learning

Despite advancements in programming education, providing adaptive learning experiences while managing cognitive load remains a challenge. Inspired by Bloom’s Taxonomy, we propose the Learn-Practice-Evaluate-Support (LPES) framework, integrated into SageJavon, an AI-powered tutoring system based on Large Language Models (LLMs). SageJavon overcomes the limitations of traditional AI tutors, such as rigid content delivery and a lack of personalized feedback. It includes four innovations: (1) an enhanced Retrieval-Augmented Generation (RAG) model for personalized resource delivery, (2) heuristic and follow-up questions to guide problem-solving, (3) adaptive knowledge tracing and recommendation models, and (4) the FUPS-Score for automatic code assessment. Deployed in a 12-week course with 85 students, SageJavon facilitated 5306 interactions in the Knowledge Q&A module and 5144 in the Programming Mentor module, with LLM-based scoring yielding average scores of 87.39 and 84.19. Compared to traditional tools, SageJavon reduces cognitive load, showing improved scores in physical demand, temporal demand, and performance (all p < .001). It also improves constructivist learning outcomes, with higher scores in prior knowledge activation (5.01 vs. 4.78), knowledge transfer (5.09 vs. 4.82), and error correction (5.24 vs. 4.68). We compare our improved RAG with other implementations and find that it outperforms the others with a 2.93% average improvement. In exercise recommendations, we achieve over 80% user satisfaction. As the number of recommended exercises increases, both SageJavon scores ((Formula presented) ) and final exam scores ((Formula presented) ) improve, demonstrating the positive impact of personalized recommendations. These results highlight SageJavon as a scalable, plug-and-play solution for programming education.