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Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework
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DOI：10.1007/s10916-026-02433-x.png)
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Abstract
En 中文
Autism spectrum disorder (ASD) affects tens of millions of families worldwide, yet parents confront abundant but unreliable online advice and limited access to timely, empathetic guidance. To address this critical gap, we developed Starmate ( http://kefeng.mpu.edu.mo/starmate ), a 1.5B-parameter, domain-tuned AI assistant for ASD caregivers, using a rigorous user-centered mixed-methods framework. Informed by in-depth interviews ( $$n=13$$ ) and a Kano survey ( $$n=60$$ ) that identified “Hands-on guidance” as a must-have caregiver requirement, we engineered a novel modular architecture that integrates sentiment analysis, expert-vetted knowledge-graph-augmented retrieval (LightRAG), and a domain-fine-tuned Qwen2.5-1.5B model. In a blinded, side-by-side comparison against leading commercial LLMs, Starmate demonstrated improved performance across key metrics within this evaluation framework (86.76 vs 78.43–83.84; $$p < 0.001$$ ) and showed specific advantages in Empathy, Hands-on guidance, and Logical clarity (all $$p < 0.05$$ ). Automated benchmarking corroborated these results, with top scores for Professional accuracy (86.18), Empathy (86.79), and Hands-on guidance (82.58). These findings demonstrate the technical feasibility of a lightweight, privacy-conscious, domain-specific LLM to generate accurate, empathetic, and actionable responses in benchmarked scenarios, laying the groundwork for future real-world usability and clinical testing.
Keywords:
Autism spectrum disorder
Large language models
Retrieval-augmented generation
User-centered design
Caregiver support
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