SHARE: Synthesizing Heterogeneous Autism-support Records into Evidence-based Recommendations

Supporting children with Autism Spectrum Disorder (ASD) requires highly individualized knowledge, but crucial information is often scattered across documents such as Individualized Education Plans (IEPs), diagnostic assessments, and caregiver notes. Thus, we propose SHARE (Synthesis of Heterogeneous Autism-support Records into Evidence-based Recommendations), a framework that transforms diverse autism-related documents into a concise, actionable set of recommendations directed towards caregivers of children with autism. Recommendations are generated with OpenAI’s large language model API, grounded in user-provided evidence with optional web-based augmentation for missing details, and each recommendation is citation-linked to ensure traceability. When caregivers rate attempted recommendations, SHARE applies a lightweight Bayesian bandit with Upper Confidence Bound (UCB) re-ranking to refine and personalize future outputs. This adaptive feedback loop sets SHARE apart from prior systems, which have focused on static goal drafting or summaries, by combining LLM-based generation, caregiver input, and interpretable ranking in a pipeline that evolves over time.