Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis
<|reference_start|>Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis: 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.<|reference_end|>
@article{cohn2024towards,
    title={Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse
  Analysis},
    author={Clayton Cohn, Caitlin Snyder, Justin Montenegro, Gautam Biswas},
    journal={arXiv preprint arXiv:2405.03677},
    year={2024},
    doi={10.1007/978-3-031-64312-5_2},
    archivePrefix={arXiv},
    eprint={2405.03677},
    primaryClass={cs.CL}
}