The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services
AI is ready to assist ABA services—step into the role of designer, implementer, or supporter now.
01Research in Context
What this study did
Cox et al. (2024) wrote a roadmap, not a lab test. They scanned the field and asked: where can AI plug into ABA right now?
The paper names three entry doors: design the tool, run the tool, or support the tool. Each door keeps the BCBA in charge.
What they found
The review found AI can sit at every step: intake, planning, staff training, in-session help, and graphing. It is already possible, not science fiction.
No data tables are given; the paper is a call to act, not a scorecard.
How this fits with other research
Yagafarova et al. (2025) give the first live score. They let an AI coach (GAINS) guide five novices running ABA trials. Four hit high fidelity, proving Cox’s "implementer" door works today.
Koegel et al. (2025) show the same for social-skills training. Their AI chatbot Noora taught autistic speakers to make empathetic statements in only four weeks. Again, the AI implementer path is real.
Jennings et al. (2024) wave the yellow flag. They list ethics gaps the BACB code still misses—data privacy, algorithm bias, and informed consent. Cox’s roadmap is exciting, but Jennings warns we need guardrails before we floor the gas.
Why it matters
You do not need to code. Start by piloting one AI add-on—maybe an automatic data graphing plug-in or an AI note-taker. Track time saved and errors caught. Share the mini-trial with your team. Small wins build buy-in and keep you ahead of the curve without breaking ethics or budget.
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02At a glance
03Original abstract
Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis. These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).
Behavior Analysis in Practice, 2024 · doi:10.1007/s40617-023-00864-3