Feasibility and Acceptability of an AI-Driven Conversational Platform for Structured Autism History Taking and Referral Support: Mixed Methods Proof-of-Concept Study.

BACKGROUND: Autism spectrum disorder is underdiagnosed in adults, with increasing demand on diagnostic services and prolonged waiting times. AI-powered tools may offer scalable solutions for early screening and triage. OBJECTIVE: This proof-of-concept study aimed to evaluate the feasibility, acceptability, and user experience of ASIST (Autism Screening With Intelligent Supportive Technology), an AI-powered conversational platform designed to support structured history taking and referral preparation for adults seeking to explore autistic traits. METHODS: A mixed methods feasibility study was conducted. Adults (N=12) interacted with a voice-based AI chatbot delivering validated screening tools (10-item Autism Spectrum Quotient and 2-Minute Autism Detection Scale). Quantitative acceptability and usability were assessed using items informed by the theoretical framework of acceptability alongside open-text qualitative feedback. RESULTS: Eleven patient and public involvement and engagement contributors informed the development and refinement of the study, and 12 adults completed the pilot evaluation. Participants generally reported positive perceptions of the chatbot, including low effort, favorable confidence, and perceived fairness. Open-text feedback highlighted the perceived value of ASIST as a history taking and referral support tool while also identifying areas for refinement, including pacing, speech clarity, and response format. CONCLUSIONS: AI-powered conversational tools may offer scalable solutions for structured history taking, referral preparation, and early triage. Further large-scale validation, pathway integration, and equity-focused evaluation are required before wider implementation.