Problem-Based Learning-Path Recommendations Through Integrating Knowledge Graphs and Large Language Models

Learning path recommendations are essential to acquire skill sets needed to solve real-life challenges. However, the main source of information that recommendation systems (RS) use to generate personalized paths is user data rather than the challenge that the user faces. In this research, we propose a problem-based approach to generate learning-path recommendations using knowledge graphs (KG) to connect learning materials, and large language models (LLM) for natural-language understanding and topic extraction. We construct a KG of courses and digital badges through human- and machine-extracted relations. Our RS analyzes a challenge written by the learner, extracts learning goals needed to solve that challenge, and then implements a Markov decision process (MDP) to select the optimal learning path. The learning path is then explained utilizing the KG and the LLM. We evaluate our KG relations in comparison to expert-defined tags. We also evaluate the recommendations and their explanations with a use-case approach. Our preliminary results show the ability of the proposed system to connect courses from different domains, recommend corresponding paths to the challenge requirements, and assign relevant explanations accordingly.