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arXiv · 2405.03677

Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis

Clayton Cohn·Caitlin Snyder·Justin Montenegro·Gautam Biswas
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Abstract

LLMs have demonstrated proficiency in contextualizing their outputs using human input, often matching or beating human-level performance on a variety of tasks. However, LLMs have not yet been used to characterize synergistic learning in students' collaborative discourse. In this exploratory work, we take a first step towards adopting a human-in-the-loop prompt engineering approach with GPT-4-Turbo to summarize and categorize students' synergistic learning during collaborative discourse. Our preliminary findings suggest GPT-4-Turbo may be able to characterize students' synergistic learning in a manner comparable to humans and that our approach warrants further investigation.

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Clayton Cohn, Caitlin Snyder, Justin Montenegro, Gautam Biswas. 2024-05-06. Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis. https://doi.org/10.1007/978-3-031-64312-5_2

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