Addressing educational overload with generative AI through dual coding and cognitive load theories

Abstract

Health professions education faces a critical challenge: the volume and complexity of medical knowledge has outpaced the cognitive limits of learners. Cognitive load theory indicates that traditional text-heavy instruction overloads the working memory. Current educational materials fail to leverage the dual coding theory which proposes that content should be presented through both verbal and visual channels. Generative AI tools enable the creation of multimodal educational content. Platforms such as ChatGPT, Gemini, NotebookLM, and HeyGen enable the creation of multimodal educational content - audio summaries, interactive mind maps, infographics, narrated videos, and voice-based interactions - designed to improve information processing in the working memory. Multimodal AI-generated content potentially enhances comprehension, retention and learning efficiency, and provides scalable, easily updated resources. These tools align with Generation Z learning preferences and can be integrated into existing curricula to transform text heavy content into multimodal engaging content. These tools can be used by faculty and learners,with minimal technical expertise or time investment. To ensure success, institutions must provide faculty and learners training, with consistent access, and evaluate outcomes. Pilot programs with iterative feedback can guide thoughtful implementation. By aligning educational strategies with cognitive science principles, generative AI can play a transformative role in addressing educational overload and creating more effective, engaging learning environments.

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