Integrating generative AI and load reduction instruction to individualize and optimize students' learning

Abstract

Generative artificial intelligence (genAI) is significantly influencing teaching and learning. The uptake of genAI in schools and universities/colleges has been rapid. But it has also been ad hoc and often ineffectively implemented, with little recognition of the need to manage cognitive burden to account for individual differences between novice and expert learners. Harnessing cognitive and instructional psychology principles, load reduction instruction (LRI) offers guidance for implementing genAI in ways that accommodate differences among novice and expert learners. LRI comprises five principles aimed at productively easing the cognitive burden on learners: (1) difficulty reduction as appropriate to prior learning, (2) support and scaffolding, (3) structured practice, (4) feedback-feedforward, and (5) independent practice and problem-solving. We suggest that the future of genAI-related learning can benefit from synthesizing genAI implementation with the core principles underpinning LRI to effectively manage the cognitive burden on diverse students as they engage with genAI to learn.