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NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners

Albert Tang, Yifan Mo, Jie Li, Yue Su, Mengyuan Zhang, Sander L. Koole, Koen Hindriks, Jiahuan Pei

Abstract

The double empathy problem frames communication difficulties between neurodivergent and neurotypical individuals as arising from mutual misunderstanding, yet most interventions focus on autistic individuals. We present NeuroWise, a multi-agent LLM-based coaching system that supports neurotypical users through stress visualization, interpretation of internal experiences, and contextual guidance. In a between-subjects study (N=30), NeuroWise was rated as helpful by all participants and showed a significant condition-time effect on deficit-based attributions (p=0.02): NeuroWise users reduced deficit framing, while baseline users shifted toward blaming autistic "deficits" after difficult interactions. NeuroWise users also completed conversations more efficiently (37% fewer turns, p=0.03). These findings suggest that AI-based interpretation can support attributional change by helping users recognize communication challenges as mutual.

NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners

Abstract

The double empathy problem frames communication difficulties between neurodivergent and neurotypical individuals as arising from mutual misunderstanding, yet most interventions focus on autistic individuals. We present NeuroWise, a multi-agent LLM-based coaching system that supports neurotypical users through stress visualization, interpretation of internal experiences, and contextual guidance. In a between-subjects study (N=30), NeuroWise was rated as helpful by all participants and showed a significant condition-time effect on deficit-based attributions (p=0.02): NeuroWise users reduced deficit framing, while baseline users shifted toward blaming autistic "deficits" after difficult interactions. NeuroWise users also completed conversations more efficiently (37% fewer turns, p=0.03). These findings suggest that AI-based interpretation can support attributional change by helping users recognize communication challenges as mutual.
Paper Structure (11 sections, 1 figure)

This paper contains 11 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Related Work
  3. Autism Communication and the Double Empathy Problem
  4. LLM-Based Social Simulation and Coaching
  5. Design and Procedure
  6. Results
  7. Prevention of Deficit-Based Attribution (RQ1)
  8. User Acceptance of Coaching Features (RQ2)
  9. Communication Efficiency (RQ3)
  10. Discussion
  11. Conclusion

Figures (1)

  • Figure 1: Condition $\times$ Time interaction for deficit framing attitudes. NeuroWise participants showed slight improvement while Baseline participants regressed toward deficit framing after difficult conversations.