Neuro-pedagogy, UDL, and AI in Teacher Education
Saleit Ron, Oranim Academic College (Israel)
Abstract
This presentation showcases the development and outcomes of “Classroom Leadership,” a specialized course designed for third-year students in the Department of English Language and Literature. Spanning two years of implementation, the presentation reviews the curriculum’s construction, bespoke learning materials, and student deliverables, all of which are anchored in a dynamic framework integrating neuro-pedagogy, Universal Design for Learning (UDL), and Artificial Intelligence (AI). Driven by the premise that “to learn is to connect,” the course emphasizes the biological roots of learning, specifically neural networks and neuroplasticity. The curriculum explores the mechanisms of the “learning brain”—from the amygdala’s emotional role to dopamine’s influence on motivation—while actively dispelling neuro-myths to elevate instructional quality. The UDL framework is utilized to align teaching methods with core brain networks: Engagement (the "Why"), Representation (the "What"), and Action & Expression (the "How"). Furthermore, the course critically addresses the integration of AI, positioning it as a tool for creativity while warning against “Cognitive Debt”—a potential decline in mental effort caused by over-reliance on technology. Through strategies like “brain breaks,” multi-sensory activities, and retrieval practice, the model ultimately empowers future educators to design inclusive, empathetic, and scientifically grounded learning environments.
Neuro-pedagogy, Universal Design for Learning (UDL), Artificial Intelligence (AI), Cognitive Debt, Neuroplasticity.
[1] Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal Design for Learning. Association for Supervision and Curriculum Development.
[2] Kosmyna, N., Hauptmann, E., Yuan, Y. T., Shi, J., Liao, X., Bereznitzky, A. V., Braunstein, I., & Maes, P. (n.d.). Accumulation of cognitive debt when using an AI assistant for essay writing. MIT Media Lab. https://arxiv.org/pdf/2506.08872v1.
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