Technology Category
Humans and AI: AI for AccessibilityNatural Language Processing: Safety and RobustnessPhilosophy and Ethics of AI: Safety, Robustness & Trustworthiness
Reimagining Support: Exploring Autistic Individuals' Visions for AI in Coping with Negative Self-Talk
🤖 AI Summary
This study investigates how autistic adults leverage artificial intelligence (AI) to mitigate negative self-talk (NST), examining their expectations, concerns, and the limitations of current AI systems in understanding neurodivergent cognition. Method: A mixed-methods approach was employed, including a quantitative survey of 200 autistic adults, in-depth interviews with clinical experts and autistic individuals, and content analysis of large language model (LLM) responses to NST intervention prompts. Contribution/Results: The study provides the first systematic integration of neurodivergent lived experience and clinical expertise. Findings indicate that LLMs demonstrate potential for NST detection and cognitive reframing but frequently produce verbose, ambiguous, and neurodiversity-informed responses—revealing implicit neurotypical biases. Based on these insights, we propose neurodiversity-optimized AI design principles, offering empirically grounded, methodologically rigorous guidance for developing trustworthy, inclusive AI tools supporting mental health.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Exploring AI's role in managing autistic individuals' negative self-talk
Assessing LLMs' effectiveness in reframing negative thoughts for autistic users
Addressing neurotypical bias in AI mental health support for autism
Innovation
Methods, ideas, or system contributions that make the work stand out.
Surveyed autistic adults on AI for negative self-talk
Analyzed LLM responses to hypothetical NST prompts
Highlighted LLM limitations for neurodivergent understanding
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