2026 ASEE Annual Conference & Exposition

This Evidence-Based Practice paper describes the design, implementation, and evaluation of an instructor training program developed for the Break Through Tech AI initiative, a national effort to broaden access to artificial intelligence (AI) education for undergraduates. As AI reshapes industry and education, the ability of instructors to create inclusive, rigorous, and engaging learning environments has become critical. To support this goal, the program developed a scalable instructor training model for its online Machine Learning Foundations course, which serves as the entry point to a year-long AI experience.

The training combines asynchronous modules with interactive, scenario-based synchronous sessions emphasizing inclusive teaching strategies, engagement in mixed-ability classrooms, and maintaining academic rigor in a supportive virtual environment. We share outcomes from pre- and post-training self-efficacy data that demonstrate gains in instructor confidence, particularly in preparing engaging online labs and using student data to support learning.

We also identify challenges, such as providing sufficient opportunities to practice responding to disengaged students, and discuss planned iterations to address them. Lessons from this experience offer practical recommendations for others developing scalable instructor training in technical and online contexts.