Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact
Research shows that dialogue, the interactive process through which participants articulate their thinking, plays a central role in constructing shared understanding, coordinating action, and shaping learning outcomes in teams. Analysing dialogue content has been central to advancing team learning theory and informing the design of computer-supported collaborative learning environments, yet this progress has depended on labour-intensive qualitative coding. LLMs offer new possibilities for automating and enhancing the dialogue layer within emerging multimodal learning analytics approaches, with recent studies showing that they can approximate human coding through few-shot prompting. Howeve...
| Authors | Kiyoshige Garces, Gloria Milena Fernandez-Nieto, Linxuan Zhao et al. |
|---|---|
| Primary category | cs.HC |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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