ASD-iLLM (Lai et al., EMNLP 2025) fine-tuned a 7B model on 8,035 ABA-based clinical dialogues and outperformed all existing models. The 'ABA with GPT' HuggingFace blueprint (Weinberger + Molnar, huggingface.co/blog/Clock070303/aba-for-gpt) provides full prompt architecture. Work: (1) Two system prompt modes in src/prompts/system.py -- Coach (in-the-moment) and Reflect (post-session metacognitive), (2) Social Stories corpus schema: fields = setting, roles, objective, sensory_profile, tone_preset, disclosure_intent, (3) boundary hygiene: agent explicitly declines diagnosis, redirects to professionals, (4) tests verifying persona differences. This is the foundation for the ASD/youth coaching product. Refs: github.com/Shuzhong-Lai/ASD-iLLM, Noora RCT doi:10.1007/s10803-025-06734-x
ASD-iLLM (Lai et al., EMNLP 2025) fine-tuned a 7B model on 8,035 ABA-based clinical dialogues and outperformed all existing models. The 'ABA with GPT' HuggingFace blueprint (Weinberger + Molnar, huggingface.co/blog/Clock070303/aba-for-gpt) provides full prompt architecture. Work: (1) Two system prompt modes in src/prompts/system.py -- Coach (in-the-moment) and Reflect (post-session metacognitive), (2) Social Stories corpus schema: fields = setting, roles, objective, sensory_profile, tone_preset, disclosure_intent, (3) boundary hygiene: agent explicitly declines diagnosis, redirects to professionals, (4) tests verifying persona differences. This is the foundation for the ASD/youth coaching product. Refs: github.com/Shuzhong-Lai/ASD-iLLM, Noora RCT doi:10.1007/s10803-025-06734-x