Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations

Understand

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.