GenAI as a Cognitive Co-Pilot in Learning Design: An Integrative Review

Abstract

This paper provides an integrative review of the intricate relationship between generative artificial intelligence (GenAI) and Cognitive Load Theory (CLT) within educational contexts. This framework utilizes foundational instructional design theories and Cognitive Load Theory (CLT) to equip learning designers with a thorough understanding. The review demonstrates that GenAI-supported instruction effectively manages intrinsic load by tailoring material complexity, reduces extraneous load through task automation like summarization and structured feedback, and enhances germane load through interactive dialogue and reflective prompts that facilitate deep processing and schema construction. The rapid integration of GenAI poses significant cognitive risks for learners. The risks encompass cognitive passivity and offloading, a reduction in productive struggle, fragmentation of attention, and an increased verification burden due to potentially inaccurate AI outputs. Poorly designed AI interfaces and excessive personalization may unintentionally increase unnecessary cognitive load. The study concludes that CLT offers critical insights for assessing and guiding the integration of GenAI in learning design. Learning designers need to deploy a systematic, theory-driven approach to effectively utilize AI's transformative potential, ensuring it enhances cognitive engagement and fosters high-quality, sustainable learning experiences rather than impeding intellectual growth.