The development of generative artificial intelligence is generally believed to replace a wide range of cognitive tasks in the workforce. This raises many questions about the role of teaching when advanced problems can be solved through an algorithm. This presentation will posit the hypothesis that future learning will be based less on increasingly advanced cognitive tasks implied through Bloom's and other taxonomies of learning, and more on adaptive skills that require increased practice and repetition. Through a brief demonstration, the case for more psycho-motor approaches will be made with an introduction to the pedagogical and practical considerations for the future of mathematics education. This will be followed by a discussion and debate about the extent to which the "cognitive assumption" is true and the consequences of taking a psychomotor approach to mathematics.

Bio: Ryan Deschamps came to mathematics honestly: his statistical package kept telling him that the ideas in his thesis draft were revolutionizing the discipline with p-values less than 0.0001%. Since working through those false positives, Ryan has embarked on a journey of interdisciplinary research in varied domains including far-right extremism, social media networks, stem-cell tourism, oceanography, quantum safety and urban studies where he had to explain Markov Chain methods, learning-with-error (LWE) lattice problems and multidimensional scaling to social scientists. Currently, he is a professor of computer science developing curriculum and teaching primarily in Conestoga College's data programs. Ryan has a PhD in public policy at the Johnson Shoyama Graduate School of Public Policy (University of Regina), and a combined MPA/MLIS at Dalhousie university.

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