This blog is a sample of our weekly newsletter, designed for ABA professionals who are building AI literacy skills. Subscribe here. Every week, we break down the foundational knowledge BCBAs and others in applied behavior analysis need to make informed decisions about the AI tools flooding our field.  

Behind the Curtain of AI Tools in ABA: What Every BCBA Needs to Know

Artificial intelligence is showing up everywhere, from session note summarizers to treatment plan generators, and many of these tools are being marketed directly to behavior analysts. Some claim to reduce clinical workload. Others say they help ensure compliance or automate decision-making. But how many of these tools are actually safe, secure, and appropriate for clinical use in applied behavior analysis (ABA)?

In this blog, we crack open the shiny shell and peek underneath the hood. Because when a tool claims to “use AI”, what that may mean is simply this: take your input (e.g., session notes, SOAP templates, behav...

You don’t need to understand algorithms or advanced math to get value from this blog. What you do need is a working understanding of what these systems do, how they behave in practice, and where they can help, or harm, your work.

As artificial intelligence (AI) continues to reshape clinical, educational, and organizational settings, professionals across the behavioral sciences, particularly Board Certified Behavior Analysts (BCBAs), face increasing pressure to evaluate and implement these technologies ethically and effectively. But not all AI is built the same. Some systems generate new content; others categorize and predict outcomes based on existing data. These differences matter deeply for clinical utility, ethical decision-making, and the design of workflows in applied behavior analysis (ABA).

To engage with artificial intelligence responsibly, behavior analysts must first understand the difference between generative and discriminative models. Knowing this distinction isn’t just ...