A Substantive–Methodological–Applied Approach to Load Reduction Instruction: Extending Theory and Evidence to Educational Practice and Generative AI
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How can teachers effectively combine direct instruction with inquiry-based approaches to learning? Load Reduction Instruction (LRI) is a teaching framework developed to address this challenge. Informed by cognitive load theory and models of information processing, LRI aims to minimize unnecessary cognitive demands while supporting the efficient acquisition and development of the knowledge and skills needed for increasingly independent problem-solving. In this presentation, Professor Martin will outline the theoretical foundations of LRI; examine the methodological approaches used to develop, test and refine the framework; review recent evidence; and highlight opportunities for its application in educational practice, including the use of generative AI to support students’ learning. The presentation is organized around a substantive–methodological–applied synergy in which (a) LRI provides the substantive focus, (b) methodological work supports its development, testing, and refinement, and (c) applied work translates these insights into educational practice, including the effective integration of generative AI into teaching and learning.
Teams link: https://teams.microsoft.com/meet/391603224522308?p=sx1QMVQPndBvL18MTZ
Bio
Andrew Martin, PhD, is Scientia Professor, Professor of Educational Psychology, and Chair of the Educational Psychology Research Group in the School of Education at the University of New South Wales, Australia. He is also Honorary Research Fellow in the Department of Education at the University of Oxford and a Registered Psychologist (Psychology Board of Australia). He specializes in student motivation, engagement, learning, and quantitative research methods. He is Consulting Editor/Editorial Board member for several international journals.
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