<p>Recommender systems have become increasingly important in online learning platforms, where learners face a growing number of available courses and substantial variation in course quality and relevance. However, many existing course recommendation methods primarily rely on user–course interaction sequences and therefore often underutilize rich course semantic information, such as descriptions of learning objectives, covered concepts, and prerequisite-related content. This limitation is particularly problematic in educational settings, where learners’ next-course choices are influenced not only by historical behavior but also by semantic relationships among courses. To address this issue, we propose LLM-CR, a recommendation framework that leverages large language models (LLMs) to enrich course representations with structured semantic information. Specifically, course metadata and textual descriptions are processed offline by an LLM to produce semantically informative course summaries, which are then encoded into dense representations and incorporated into the recommendation pipeline through a lightweight fusion module. In this way, LLM-CR augments conventional behavioral representations with course semantics related to topics, knowledge progression, and prerequisite-relevant information. The resulting model is evaluated in the standard next-course recommendation setting using ranking-based metrics. Experiments on six subject-specific subsets constructed from the XuetangX platform show that LLM-CR consistently improves recommendation performance over strong baseline methods, with especially notable gains on relatively sparse datasets. Additional analyses indicate that the proposed semantic augmentation introduces only modest extra complexity because the most expensive LLM processing is performed offline and reused across training and inference. These results suggest that incorporating LLM-derived semantic features is an effective and practical way to improve course recommendation quality.</p>