2026 ASEE Annual Conference & Exposition

The letters “AI” elicit various responses from faculty members, ranging from a pessimistic fear of how it may be abused by students to optimistic views of it improving the quality of student work and beyond. Many see potential value in generative artificial intelligence (gAI) but have seen enough issues with hallucinations and other shortcomings of gAI to be discouraged. For others, the increasing number of choices of gAIs available make it difficult to determine how to proceed to make gAI useful in chemical engineering teaching and learning. In addition, while institutions may enact policies around gAI, faculty are rarely provided with guidance for effectively utilizing gAI to facilitate course preparation and to address student learning outcomes.
This work-in-progress report describes preliminary results from a project intended to provide guidance to faculty on how to efficiently use gAI to generate student-facing resources in a short period of time based on observations of student misconceptions. Using a simple process DRIVE of Define (a target learning outcome), Reason (identify a suitable approach to achieving those outcomes), Innovate (by using AI to synthesize a resource), Validate (ensure the resource is correct and useful), and Evaluate (whether the resource impacts learning). A maximum of 1 hour per topic (and typically much less time) is spent with AI to develop a resource to address a specific student learning challenge. The process typically results in a resource that, upon validation, can be immediately deployed to an LMS for use by students and then evaluated for effectiveness. The paper will describe some of the examples developed to date provide introductory guidance for those seeking to use common commercial AIs as part of their instructional development process.