Traditional learning methodologies often fall short of accommodating diverse learner needs and adapting dynamically to individual learning paces and styles. This limitation underscores the growing need for personalized learning, which has the potential to significantly improve learning outcomes, foster deeper engagement, and enhance learner motivation. This study introduces a novel personalized recommendation framework (PRF) that leverages large language models (LLMs) and chain-of-thought (CoT) prompting techniques to advance personalized learning. Specifically, it proposes a strategic personalization framework that addresses learner heterogeneity by incorporating both preference-based and performance-based features. CoT prompting is integrated to simulate human-like sequential reasoning in LLMs, thereby improving the framework’s adaptability and effectiveness. A case study was conducted in a computer programming course, a domain that requires both conceptual understanding and practical problem-solving, to evaluate the proposed framework. The assessment involved 15 expert reviewers who examined the framework’s effectiveness and overall satisfaction. Experimental results showed that the proposed PRF generated recommendations perceived as significantly more satisfactory than those produced by the non-PRF system (M = 4.50 ± 0.30 vs. 3.73 ± 0.21, p < 0.001). In addition, the experts strongly agreed that the framework effectively identified students in urgent need of support, provided timely recommendations, and delivered personalized learning experiences aligned with individual learner needs.
Hongthong et al. (Fri,) studied this question.