Reimagining Support: Exploring Autistic Individuals' Visions for AI in Coping with Negative Self-Talk

要旨

Autistic individuals often experience negative self-talk (NST), leading to increased anxiety and depression. While therapy is recommended, it presents challenges for many autistic individuals. Meanwhile, a growing number are turning to large language models (LLMs) for mental health support. To understand how autistic individuals perceive AI's role in coping with NST, we surveyed 200 autistic adults and interviewed practitioners. We also analyzed LLM responses to participants' hypothetical prompts about their NST. Our findings show that participants view LLMs as useful for managing NST by identifying and reframing negative thoughts. Both participants and practitioners recognize AI's potential to support therapy and emotional expression. Participants also expressed concerns about LLMs' understanding of neurodivergent thought patterns, particularly due to the neurotypical bias of LLMs. Practitioners critiqued LLMs' responses as overly wordy, vague, and overwhelming. This study contributes to the growing research on AI-assisted mental health support, with specific insights for supporting the autistic community.

著者
Buse Carik
Virginia Tech, Blacksburg, Virginia, United States
Victoria V. Izaac
Virginia Tech, Blacksburg, Virginia, United States
Xiaohan Ding
Virginia Tech, Blacksburg, Virginia, United States
Angela Scarpa
Virginia Tech, Blacksburg, Virginia, United States
Eugenia H. Rho
Virginia Tech, Blacksburg, Virginia, United States
DOI

10.1145/3706598.3714287

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714287

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Neurological Considerations

G414+G415
7 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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