ASD -i LLM :An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025)

Abstract: Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, particularly when directly employing LLMs as an intervention doctor. Researchers have mainly focused on using prompt engineering for role play as an intervention doctor and integrating auxiliary elements such as visual stimuli to enhance the sensory experience of the intervention, while neglecting the challenge that LLMs’ inherent dialogue style and intervention strategies do not meet the requirements of clinical dialogue interventions. To fill the gap, we propose a comprehensive framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) which is commonly used by clinicians. Specifically, we collected clinical recordings of dialogue interventions for autistic children and constructed the topic dialogue dataset ASD-iLLM-8k. By incorporating the system prompt based on the ABA and ASD-iLLM-8k dataset, we fine-tuned LLMs to develop ASD-iLLM. We also proposed a role-play strategy in which LLMs act as autistic children to comprehensively evaluate the doctor model’s capabilities at the dialogue level. Extensive experiments indicate that ASD-iLLM outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors. Our dataset, model, and code are available on https://github.com/Shuzhong-Lai/ASD-iLLM.

## introduction autism spectrum disorder (asd) is one of the most common heterogeneous neurodevelopmental disorders in children, characterized by social * corresponding author: lin.yao@zju.edu.cn figure 1 : an example from the test set which displays the responses of the human therapist, asd-illm, and gpt-4.1 about the same dialogue history. the global prevalence of autism is rising annually, reaching approximately 1% @xcite . although timely diagnosis and treatment can significantly improve the core symptoms of individuals with autism @xcite , this often requires years of effort and a substantial financial burden. moreover, due to uneven healthcare resources across regions, a considerable number of children do not receive effective interventions, leading to social disconnection and living independently @xcite . therefore, there is an urgent need for the emergence of new forms of intervention. with the rise of large language models (llms), researchers believe that llms' capabilities hold promise for application in autism therapy @xcite @xcite . current research primarily concentrates on two types. firstly, augmenting various aspects of existing interventions with llm capabilities. for example, providing professional advice to parents @xcite @xcite or offering cues to assist children in communication @xcite . however, these methods merely indirectly influence the treatment of autistic children and cannot serve as an intervention. secondly, investigating whether llms can substitute clinicians in delivering interventions @xcite @xcite . but these methods only constrain llms to act as intervention doctors through prompt engineering, and their conversational style and intervention strategies still differ significantly from real clinical intervention dialogues, as shown in figure 1 . to address this, we propose autism spectrum disorder intervention large language model (asd-illm), which is designed for topic dialogue intervention for autistic children. we collected real clinical intervention dialogue recordings of autistic children and constructed a multi-turn dialogue dataset, asd-illm-8k. following aba principles, we designed the system prompt and used asd-illm-8k for fine-tuning on llms to learn the conversational style and intervention strategies used by doctors. results from both automatic and human evaluations indicate that asd-illm outperforms existing state-of-the-art (sota) llms in topic dialogue intervention tasks for autistic children. the whole framework for asd-illm is shown in figure 2 . our contributions are as follows: ## related work with the substantial increase in the scale and capabilities of llms, more researchers are exploring the application of llms in autism treatment @xcite ). llm's conversational and instruction-following abilities allow it to adapt to various dialogue scenarios. for adults with autism, the researchers @xcite employ virtual reality (vr) to create scenarios and utilize llms for dialogue generation, aiding them in practicing communication skills for job seeking. additionally, @xcite constructed interactive scenarios for intervention using the nao robot equipped with gpt-2 @xcite , and assessed its effec-tiveness through conversations with experts. echo-teddy @xcite developed a social dialogue robot shaped as a toy teddy bear, embedded with the llm for conversation, showcasing the potential in supporting children. in addressing social impairments, chatasd therapist (ren et al., 2023) utilizes gpt-4 @xcite and facial video generation technology for intervention, while asd-chat @xcite employs a design paradigm integrating verbal behavior milestones assessment and placement program (vb-mapp) @xcite and chatgpt for topic dialogue interventions. @xcite created a dataset of user-consultant interactions for fine-tuning falcon7b @xcite and integrated yolov5 @xcite and @xcite to enhance visual ability. however, the paper lacks detailed information about the dataset and rigorous quantitative analysis. for emotional support, emoeden @xcite utilizes gpt-4 for dialogue and employs midjourney to generate conversational scenarios, assisting children in emotion recognition and expression training. ## dataset our goal is to modify the conversational style and intervention strategy of llm to more closely resemble real clinical dialogue intervention scenarios. however, there is currently no publicly available dataset for autism dialogue interventions. therefore, we have created a multi-turn dialogue dataset for intervention between doctors and autistic children, named asd-illm-8k. ## conclusion in this study, we developed a comprehensive framework for constructing llms in dialogue intervention for autistic children, encompassing data collection, model training, and evaluation. we developed asd-illm-8k, the first available chinese dataset for dialogue intervention in autism, to advance future research. our model, asd-illm, is compact yet powerful, addressing the shortcomings of existing models whose dialogue styles do not closely align with clinical interventions, and intervention strategies do not adhere to aba principles. extensive experiments demonstrate that our proposed framework is effective, the dataset is of high quality, and the model exhibits excellent capabilities in performing various aspects of dialogue intervention tasks for autistic children.