arXiv cs.AI· Eric Rudolph, Philipp Steigerwald, Jens Albrecht·· 14 小时前AI 评分22
人类角色扮演者与 LLM 模拟来访者在在线咨询培训中的对比：咨询沟通模式分析
Comparing Human Role-Players and LLM-Simulated Clients in Online Counselling Training: An Analysis of Counselling Patterns
AI 导读
研究对比了人类角色扮演者与 Mistral Mixtral 8x7b（4-bit 量化）模拟来访者在咨询培训中的表现，用句子级咨询沟通模式系统分析互动。研究考察两点：LLM 生成回复在多大程度上复现真实对话动态，以及咨询师面对人类与 LLM 模拟来访者时沟通行为是否不同。结果显示两种场景下咨询模式的应用既有相似也有差异，LLM 角色扮演练习有提升咨询胜任力的潜力。
正文 · 原文
Abstract:This study investigates the capabilities of Large Language Models to simulate counselling clients in educational role-plays in comparison to human role-players. Initially, we recorded role-playing sessions, where novice counsellors interacted with human peers acting as clients, followed by role-plays between humans and clients simulated by Mistrals Mixtral 8x7b using 4-bit quantization. These interactions were analysed with a counselling communication pattern system at sentence level. We investigated two key questions: (1) to what extent LLM-generated responses replicate authentic conversational dynamics and (2) whether counsellors' communication behaviour differs when interacting with human versus LLM-simulated clients. The findings highlight both similarities and differences in the application of counselling patterns across scenarios, showing the potential of LLM-based role-playing exercises to enhance counselling competencies and to identify areas for further refinement in virtual client simulations.
| Comments: | Published at EDM 2025 (18th International Conference on Educational Data Mining), short paper. 7 pages |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.40563 [cs.AI] |
| (or arXiv:2610.40563v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40563 arXiv-issued DOI via DataCite |
|
| Journal reference: | Proceedings of the 18th International Conference on Educational Data Mining (EDM 2025), short papers |
| Related DOI: | https://doi.org/10.5281/zenodo.15870260
DOI(s) linking to related resources |
Submission history
From: Eric Rudolph [view email]
[v1]
Thu, 29 Oct 2026 17:41:59 UTC (9,039 KB)
来源：arXiv cs.AI · arxiv.org