Accepted for/Published in: JMIR Formative Research
Date Submitted: May 4, 2026
Date Accepted: Aug 11, 2026
Feasibility and Acceptability of ASIST, an AI-Driven Conversational Platform for Structured Autism History-Taking and Referral Support: A Mixed-Methods Proof-of-Concept Study
ABSTRACT
Background:
Autism Spectrum Disorder (ASD) is underdiagnosed in adults, with increasing demand on diagnostic services and prolonged waiting times. Artificial intelligence (AI)-powered tools may offer scalable solutions for early screening and triage.
Objective:
This study aimed to evaluate the feasibility, acceptability, and early user experience of AI-powered autism screening in adults through a proof-of-concept evaluation of ASIST, a conversational AI system developed on the SimFlow platform.
Methods:
A mixed-methods feasibility study was conducted. Adults ($n=12$) interacted with a voice-based AI chatbot delivering validated screening tools (AQ-10 and 2MADS). Quantitative acceptability and usability were assessed using items informed by the Theoretical Framework of Acceptability (TFA), alongside open-text qualitative feedback.
Results:
Eleven PPIE 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, favourable confidence, and perceived fairness. Open-text feedback highlighted the perceived value of ASIST as a pre-screening tool, while also identifying areas for refinement, including pacing, speech clarity, and response format.
Conclusions:
AI-powered tools such as ASIST show promise as pre-screening and referral-support systems for adult autism pathways. ASIST was not designed or evaluated as a diagnostic tool. Further large-scale validation, pathway integration, and equity-focused evaluation are required before wider implementation.
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