A central challenge in knowledge transfer lies in the transfer of tacit knowledge. LLMs, capable of identifying latent patterns in data, present an interesting opportunity to address this issue. This paper explores the potential of LLMs to externalize experts’ tacit knowledge and aid its transfer to novices. Specifically, we examine three questions: RQ1: Can LLMs effectively externalize experts’ tacit knowledge? How to do so (e.g., prompting strategy)? RQ2: How can LLMs use externalized tacit knowledge to make effective decisions? RQ3: How can LLM-externalized tacit knowledge support novice learning? We explore these questions using real-world tutoring conversations collected by Wang et al. (2024).

Our findings suggest that LLMs may be capturing nuances from experts’ observed behavior that are different from the knowledge experts articulate. With carefully designed prompting strategies, LLMs may offer a practical and scalable means of externalizing and transferring tacit knowledge.