🤖 AI Summary
Access to real-world Applied Behavior Analysis (ABA) session data is severely limited by privacy constraints, hindering the training of AI models in this domain. To address this challenge, this work proposes a deterministic synthetic data generation method grounded in authoritative ABA taxonomies, enabling—for the first time—the construction of fully traceable instruction-tuning datasets. The approach supports two core tasks: instructional program generation and multi-session behavioral trajectory interpretation, while integrating standard ABA paradigms such as Discrete Trial Teaching and Natural Environment Teaching. The resulting TRACE dataset comprises 2,999 structurally transparent and content-compliant samples, partitioned into training, validation, test, and reasonableness-check splits according to predefined ratios. Both code and data are publicly released under CC BY-NC 4.0 and MIT licenses.
📝 Abstract
Applied Behavior Analysis (ABA) is a clinical discipline whose documentation, teaching programs and multi-session behavioral logs, is formulaic and high-volume, yet real session data is HIPAA-protected and bound by professional confidentiality rules, blocking the release of a training corpus. We present TRACE (Taxonomy-Referenced ABA Clinical Examples), a 2,999-example synthetic instruction-tuning dataset covering two ABA tasks: teaching-program generation across Discrete Trial Training, Natural Environment Teaching, and Task Analysis; and multi-session behavioral interpretation across twelve trajectory patterns and thirteen target behaviors. Every example is produced by a deterministic taxonomy-driven generator grounded in the canonical ABA literature, and every example carries complete sampling provenance, the exact taxonomy cells that produced it. The dataset is released under CC BY-NC 4.0 for data and MIT for code, with stratified train (2,549), validation (149), test (281), and sanity (20) splits. TRACE is a research artifact and has not been clinically validated.