Effective and Transparent Course Recommendation through Causal Reasoning with Language Models

Recommender systems are crucial to support learners through the abundance of available online educational resources. Recent advances in educational recommendation have employed knowledge graphs to enhance both the effectiveness and transparency of recommendations. However, these systems primarily rely on correlational reasoning. Such approaches generate user-aligned suggestions through path-based explanations but often fail to capture the underlying, true causal relationships that drive educational progress and decision-making, which inherently depend on the instantiated knowledge graphs. In this paper, we discuss our ongoing efforts in developing reasoning methods in course recommendation and how augmenting models with causal relationships can transform the way recommendations are generated and explained. We discuss the importance of causal inference for developing effective and transparent systems that can recommend not just what other learners with similar profiles choose, but what a learner should study next based on other covariates such as the learning history and context.