# Comparing Human Role-Players and LLM-Simulated Clients in Online Counselling Training: An Analysis of Counselling Patterns

**Authors:** Eric Rudolph, Philipp Steigerwald, Jens Albrecht  
**Lab:** Georg Simon Ohm University of Applied Sciences Nuremberg · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40563) · [PDF](https://arxiv.org/pdf/2610.40563) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40563.html)

## Key numbers

- **4-bit**: followed by role-plays between humans and clients simulated by Mistrals Mixtral 8x7b using 4-bit quantization

## 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.

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