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

arXiv: https://arxiv.org/abs/2405.03677 · HTML: https://mlsanity.com/paper/2405.03677/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approaches](https://mlsanity.com/paper/2401.01692/) (2024)

## Built on by (most cited first, 7 of 7)

- [Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2505.17238/) (2025)
- [CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback](https://mlsanity.com/paper/2504.02323/) (2025)
- [Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review](https://mlsanity.com/paper/2408.14491/) (2024)
- [Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agents](https://mlsanity.com/paper/2602.01415/) (2026)
- [Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learning](https://mlsanity.com/paper/2601.04458/) (2026)
- [Enhancing LLM-Based Data Annotation with Error Decomposition](https://mlsanity.com/paper/2601.11920/) (2026)
- [From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP](https://mlsanity.com/paper/2605.25226/) (2026)

## Abstract (arXiv)

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.