| Abstract: Artificial Intelligence has become a central part of understanding the teaching and learning processes in education (Nafea, 2017). In education, machine learning has been touted as a way for educators to improve efficiency and to personalize the learning that students and teachers undertake (Petrilli, 2018). As more efficient methods of teaching and practice are sought to improve the way we approach behavioral analytic training, such as virtual and mixed reality, a single question remains, “When will behavior analysis get bit by the artificial intelligence bug?” Using data from the author’s systematic review on functional behavior assessment, the authors will walk participants through “FBAware” a simulated application that can detect if a behavior analyst has bias in determining the function of behavior. Next, the authors will present sample linear models, R^2, and adjusted R^2 values of the predictive models as a use case for what a predictive analytics could look like for organizations and universities. Lastly, the authors will talk about the promise and pitfalls that predictive analytics could bring in indicating where bias exists and how the field can become forward thinking in order to shape a future where artificial intelligence and behavior analysis coincide. |