Transforming Applied Behavior Analysis Therapy: An Internet of Things-Guided, Retrieval-Augmented Large Language Model Framework

We propose ABA-RAG, a retrieval-augmented generation (RAG) framework specifically tailored for applied behavior analysis (ABA) interventions, which integrates real-time emotional and behavioral data from Internet-of-Things (IoT) wearable devices. In ABA-RAG, we systematically retrieve semantically relevant prompts from a structured ABA task repository using embedding-based semantic search and dynamically construct context-aware prompts by incorporating IoT-derived predictions of learners’ emotional and behavioral states. To efficiently adapt large language model (LLM) to ABA contexts, we evaluate and compare several low-rank fine-tuning methods—including Low-Rank Adaptation (LoRA), Butterfly Orthogonal Fine-Tuning (BOFT), and LoRA with Gradient Approximation (LoRA-GA). Among these techniques, LoRA-GA demonstrates the best balance between computational efficiency and generation quality, making it particularly suitable for resource-constrained environments. Comprehensive experiments, validated through rigorous quantitative metrics and expert ABA practitioner evaluations, demonstrate that ABA-RAG significantly reduces manual workload while enhancing the precision, contextual relevance, and clinical utility of generated ABA tasks. Integrated into a practical web-based system, ABA-RAG provides ABA professionals with a scalable and real-time platform to generate individualized interventions.