Paradigm Shift? Preliminary Findings of Engineering Faculty Members’ Mental Models of Assessment in the Era of Generative AI

Plain-Language Summary

This preliminary study examines how engineering faculty members reported that generative AI affected their thinking about assessment. It connects those responses to demographic, disciplinary, position, experience, and course-context patterns.

Research Question

How do engineering faculty members' responses to the arrival of generative AI in their assessment practices vary based on their demographics and professional contexts?

• Analyzed responses from 67 engineering faculty members who answered an event-survey question about whether generative AI had affected their thinking about assessment.
• Matched yes, no, maybe, and uncertain responses with demographic and professional-background data from a broader mental models study.
• Used descriptive comparisons across gender, race, department, position, years of experience, and course type.

• More than half of respondents reported that generative AI had not affected their thinking about assessment, while 27 reported that it had.
• Response patterns varied across course types, with first-year engineering instructors appearing especially receptive to generative AI's influence.
• The findings suggest variation by position, experience, and teaching context, but the paper emphasizes that larger samples are needed before making broad claims.