Document detail
ID
oai:arXiv.org:2405.03677
Topic
Computer Science - Computation and...Author
Cohn, Clayton Snyder, Caitlin Montenegro, Justin Biswas, GautamCategory
Computer Science
Year
2024
listing date
7/10/2024
Keywords
approach discourse collaborative students learning synergisticAbstract
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.
;Comment: In press at the 25th international conference on Artificial Intelligence in Education (AIED) Late-Breaking Results (LBR) track
Cohn, Clayton,Snyder, Caitlin,Montenegro, Justin,Biswas, Gautam, 2024, Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis
