Andrew Katz
Virginia Polytechnic Institute and State University, VA·Education Research·Early-career
What should engineers learn, and how should engineering be taught now that generative artificial intelligence (AI), software that can write text, code and solutions on request, is changing both classrooms and workplaces? This research looks at engineering education from two sides.
- Fields
Education Research
Engineering
Computer Science
- Topics
engineering education
generative AI
natural language processing
- Grants
- 2 known grants, through Aug 2029
What they work on
- Extract the skills employers request from more than a decade of engineering job postings, using natural language processing (computer analysis of text) and a skill-extraction algorithm focused on mechanical engineering master's roles.
- Interview current mechanical engineering master's students about why they pursue the degree and which skills they hope to gain, then compare their answers with employer demands to identify gaps.
- Characterize engineering faculty members' mental models of generative AI, meaning their beliefs about how it works and what it can do, through in-depth interviews with faculty and AI experts.
- Track how faculty beliefs about generative AI and their assessment decisions change over time, using an annual nationally representative survey analyzed with statistics and large-scale text analysis.
More about the research
One project uses computational text analysis to compare the skills employers ask for with the goals of master's students. The other studies how engineering professors think about generative AI and how those beliefs shape the way they test students.
Why it matters
Engineering master's graduates make up a large part of the technical workforce. Clearer data on which skills employers want could help universities design graduate courses and attract more domestic students into these programs. Understanding how professors view generative AI could guide faculty training and assessment practices, so that engineering students still master core concepts while also learning to use AI tools well.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
CAREER: Minds and Machines: Exploring Engineering Faculty Member Mental Models of Generative AI and Instructional Decisions
NSF · Sep 2024 – Aug 2029
Collaborative Research: Research: The Engineering Master’s Workforce: Leveraging Natural Language Processing Techniques to Understand Employer Demands and Student Goals
NSF · Jan 2025 – Dec 2027
Closest labs
Maura Borrego
University of Texas at Austin, TX·Education Research
Extract the skills employers want from engineering master's degree holders by applying natural language processing (NLP), including generative AI, to more than a decade of engineering job postings.
Glen Miller
Texas A&M University-College Station, TX·Education Research
Catalog current and planned AI ethics education interventions across diverse U.S. colleges, including community colleges and minority-serving institutions, through a national survey.
Stephanos Matsumoto
Franklin W Olin College of Engineering, MA·Education Research
Survey undergraduate students and practitioners in mechanical, electrical and civil engineering to identify which software engineering practices and tools (SEPTs) they use, using an instrument grounded in the SWEBOK Guide and SE2014 guidelines.
The school
Virginia Polytechnic Institute and State University: 35 labs on PI Pages, $657M a year on research, 496 research doctorates a year.
More about Virginia Polytechnic Institute and State University
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