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Autores principales: Tang, Albert, Mo, Yifan, Li, Jie, Su, Yue, Zhang, Mengyuan, Koole, Sander L., Hindriks, Koen, Pei, Jiahuan
Formato: Preprint
Publicado: 2026
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Acceso en línea:https://arxiv.org/abs/2602.18962
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author Tang, Albert
Mo, Yifan
Li, Jie
Su, Yue
Zhang, Mengyuan
Koole, Sander L.
Hindriks, Koen
Pei, Jiahuan
author_facet Tang, Albert
Mo, Yifan
Li, Jie
Su, Yue
Zhang, Mengyuan
Koole, Sander L.
Hindriks, Koen
Pei, Jiahuan
contents 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.
format Preprint
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publishDate 2026
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spellingShingle NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners
Tang, Albert
Mo, Yifan
Li, Jie
Su, Yue
Zhang, Mengyuan
Koole, Sander L.
Hindriks, Koen
Pei, Jiahuan
Human-Computer Interaction
Artificial Intelligence
Computers and Society
Information Retrieval
Multiagent Systems
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.
title NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners
topic Human-Computer Interaction
Artificial Intelligence
Computers and Society
Information Retrieval
Multiagent Systems
url https://arxiv.org/abs/2602.18962