In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of a great value to enhance the learner's understanding and engagement with the recommended learning content. Large language models (LLMs) and generative AI in general have recently opened new doors for generating human-like explanations, for and along learning recommendations. However, their precision is still far away from acceptable in a sensitive field like education. To harness the abilities of LLMs, while still ensuring a high level of precision towards the intent of the learners, this paper proposes an approach to utilize knowledge graphs (KG) as a source of factual context, for LLM prompts, reducing the risk of model hallucinations, and safeguarding against wrong or imprecise information, while maintaining an application-intended learning context. We utilize the semantic relations in the knowledge graph to offer curated knowledge about learning recommendations. With domain-experts in the loop, we design the explanation as a textual template, which is filled and completed by the LLM. Domain experts were integrated in the prompt engineering phase as part of a study, to ensure that explanations include information that is relevant to the learner. We evaluate our approach quantitatively using Rouge-N and Rouge-L measures, as well as qualitatively with experts and learners. Our results show an enhanced recall and precision of the generated explanations compared to those generated solely by the GPT model, with a greatly reduced risk of generating imprecise information in the final learning explanation.

翻译：在个性化教育时代，为学习推荐提供易于理解的解释对于增强学习者对推荐学习内容的理解和参与度具有重要价值。大语言模型（LLM）及生成式人工智能普遍为学习推荐生成类似人类的解释开辟了新途径。然而，其精确性在教育这类敏感领域仍远未达到可接受水平。为在确保对学习者意图高度精准的同时发挥LLM的能力，本文提出一种方法：将知识图谱（KG）作为LLM提示词的事实依据上下文来源，从而降低模型幻觉风险，防止错误或不精确信息，同时保持面向应用的学习情境。我们利用知识图谱中的语义关系来提供关于学习推荐的精选知识。在领域专家的参与下，我们将解释设计为文本模板，由LLM填充完善。作为研究的一部分，领域专家参与提示词工程阶段，以确保解释包含对学习者相关的信息。我们通过Rouge-N和Rouge-L指标进行定量评估，并邀请专家和学习者进行定性评估。结果表明，与仅由GPT模型生成的解释相比，我们生成的解释在召回率和精确率上均有提升，且最终学习解释中出现不精确信息的风险显著降低。