WIP: The Challenge of Any Technology? Intergenerational Sense-Making About GenAI Systems in Engineering Education
Plain Language Summary
As generative AI (GenAI) tools like ChatGPT spread through universities, engineering instructors are not only deciding how to use them in their own courses but also forming opinions about how their colleagues and students use them. In interviews with engineering instructors, conversations often turned to age and generation, with instructors describing differences between themselves and older or younger colleagues, and between themselves and students who are growing up with these tools. In this work-in-progress paper, we share early findings about those generational tensions, which matter because age is rarely discussed as part of diversity among engineering faculty, even though it may shape how engineering programs respond to GenAI.
Contribution
Where studies of GenAI in engineering education have largely treated instructors' adoption as an individual or institutional matter, we foreground age and generation, an under-examined dimension of diversity among engineering instructors, as a lens through which instructors judge colleagues' and students' orientations toward GenAI systems and, in turn, inform how their programs approach them.
Methods
We draw on an NSF CAREER project examining how nearly 170 engineering instructors at 18 U.S.-based institutions use and think about GenAI systems for teaching, learning, and assessment. Data comprise hour-long, semi-structured interviews, during which instructors frequently raised generational and age-related aspects of GenAI diffusion unprompted; this work-in-progress examines those moments as preliminary evidence of intergenerational sense-making.