Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis
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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.
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
4 of 12 references.
References · 4
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2024A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Sciencecited by 31
- 2024Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approachescited by 8
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Citing papers per year: 2024: 1; 2025: 2; 2026: 4.
7 papers.
- 2025Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)cited by 6
- 2025CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedbackcited by 5
- 2024Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Reviewcited by 4
- 2026Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agentscited by 3
- 2026Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learningcited by 1
- 2026Enhancing LLM-Based Data Annotation with Error Decompositioncited by 1
- 2026From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLPcited by 0
- 2026From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP24 May 2026
- 2026Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agents1 Feb 2026
- 2026Enhancing LLM-Based Data Annotation with Error Decomposition17 Jan 2026
- 2026Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learning8 Jan 2026
- 2025Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)22 May 2025
- 2025CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback3 Apr 2025
- 2024Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review22 Aug 2024