Embracing the illusion of explanatory depth: A strategic framework for using iterative prompting for integrating large language models in healthcare education

Abstract

Healthcare educators are exploring ways to integrate Large Language Models (LLMs) into the curriculum. At the same time, they are concerned about the negative impact on students’ cognitive development. There is concern that the students will not learn to think and problem-solve by themselves and instead become dependent on LLMs to find answers. In addition, the students could start accepting the LLM generated responses at face value. The Illusion of Explanatory Depth (IoED) is a cognitive bias where humans believe they understand complex phenomena in more depth than they do. This illusion is caused when people rely on external sources of information rather than deeper levels of internalized knowledge. This illusion can be exposed by asking follow-up in depth questions. Using the same approach, specifically iterative prompting, can help students interact with LLM’s while learning actively, gaining deeper levels of knowledge, and exposing the LLM shortcomings. The article proposes that educators encourage use of LLMs to complete assignments using a template, that promotes students’ reflections on their interactions with LLMs, using iterative prompting. This process based on IoED, and iterative prompting will help educators integrate LLMs in the curriculum while mitigating the risk of students becoming dependent on these tools. Students will practice active learning and experience firsthand the inaccuracies and inconsistencies in LLM responses.