Bookmark: Scaling evidence-based instructional design expertise using AI

In “Scaling Evidence-based Instructional Design Expertise Using AI,” the research spearheaded by Gautam Yadav’s team at Carnegie Mellon University examines the transformative potential of AI in instructional design, particularly using Large Language Models (LLMs) like GPT-4 to bridge the gap between educational theory and practical application. The central thesis revolves around the capability of AI to scale evidence-based instructional practices traditionally limited by resources. Through two pivotal experiments, the study showcases AI’s ability to streamline the development of educational content. In the first experiment, AI was used to generate varied scenarios for an e-learning course by leveraging a single exemplar, significantly reducing development time while preserving quality through expert review. The second experiment engaged AI as a partner in creating hands-on programming assignments, revealing a need for multiple examples to achieve desired outcomes. This research underscores the necessity of instructional expertise for effective AI integration, highlighting the potential of specialized AI tools tailored for instructional design which could offer a more nuanced and efficient collaboration compared to general AI systems Scaling Evidence-based Instructional Design Expertise Using AI