Integrating Generative AI into Instructional Design Practice: Effects on Graduate Student Learning and Self-Efficacy

Steven Moore, Lydia Eckstein, Christine Kwon, John C. Stamper

Abstract: Generative AI (genAI) tools are increasingly being integrated into instructional design workflows for content creation, assessment development, and lesson planning. For novice designers, it is critical to understand whether this integration supports the design process without compromising underlying pedagogical learning. This study addresses this gap through an ecologically valid field experiment within a 14-week graduate course training novice instructional designers. Using a counterbalanced A/B design embedded in authentic coursework, students created eight microlessons, alternating genAI assistance with independent work. Learning of pedagogical principles was assessed via module pre/post-tests and self-efficacy, in teaching practices and genAI use, was measured via course-level pre/post-surveys. Results showed no evidence that genAI use hindered learning, as post-test scores on module content remained stable or improved, despite variations in test form difficulty. Students demonstrated substantial and statistically significant gains by over 10% in self-efficacy related to both their teaching practices and their ability to leverage genAI. By evaluating genAI within sustained and authentic instructional design activities, this study demonstrates that thoughtfully integrated AI tools can effectively support novice designers, enhancing their confidence and professional growth without compromising foundational pedagogical learning.

External IDs:dblp:conf/ectel/MooreEKS25