How do engineering faculty understand generative AI, and how do those mental models shape instructional decisions?

Approach

• Interview engineering faculty about how they conceptualize generative AI tools and classroom use cases
• Model how beliefs about AI capabilities, risks, and responsibility connect to instructional choices
• Translate findings into practical guidance for responsible AI integration in engineering education

Objectives

• Understand how engineering faculty conceptualize AI tools
• Develop frameworks for AI integration in engineering education
• Create guidelines for responsible AI use in teaching

Evidence and materials

NSF CAREER award

Five-year National Science Foundation CAREER project supporting research on faculty mental models of generative AI and instructional decision-making. Status: Available.

Faculty interview protocol

Semi-structured interview materials for eliciting faculty mental models, perceived risks, and instructional decision patterns. Status: Restricted.

Tools from this project

• Self-Regulated Teaching Intelligence
• Cadence
• Adaptive AI Interviewer

Data, code, and media

• Slides: Faculty Development Workshops (2025)

Contact for access to restricted interview materials.

Team

PI

• Andrew Katz

Graduate Research Assistants

• Benjamin E. Chaback
• Mitch Gerhardt