  This paper presents a comprehensive exploration of leveraging Large Language
Models (LLMs), specifically GPT-4, in the field of instructional design. With a
focus on scaling evidence-based instructional design expertise, our research
aims to bridge the gap between theoretical educational studies and practical
implementation. We discuss the benefits and limitations of AI-driven content
generation, emphasizing the necessity of human oversight in ensuring the
quality of educational materials. This work is elucidated through two detailed
case studies where we applied GPT-4 in creating complex higher-order
assessments and active learning components for different courses. From our
experiences, we provide best practices for effectively using LLMs in
instructional design tasks, such as utilizing templates, fine-tuning, handling
unexpected output, implementing LLM chains, citing references, evaluating
output, creating rubrics, grading, and generating distractors. We also share
our vision of a future recommendation system, where a customized GPT-4 extracts
instructional design principles from educational studies and creates
personalized, evidence-supported strategies for users' unique educational
contexts. Our research contributes to understanding and optimally harnessing
the potential of AI-driven language models in enhancing educational outcomes.

Computation and Language;Human-Computer Interaction