Interview Graph
Coding interview transcripts into a property graph, so that a claim and the evidence for it stay attached to each other.
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portfolio
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Purrcept
A local workbench for asking language models the same decision questions of every passage in a corpus, and for finding out whether the answers can be trusted.
publications
Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching
Published in International Journal of Qualitative Methods, 2024
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Recommended citation: Katz, A., Gerhardt, M., & Soledad, M. (2024). Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching. International Journal of Qualitative Methods, 23, 16094069241293283. https://doi.org/10.1177/16094069241293283
Reimagining Behavioral Analysis in Engineering Education: A Theoretical Exploration of Reasoned Action Approach
Published in In the proceedings of 2024 ASEE Annual Conference & Exposition, 2024
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Recommended citation: Gerhardt, M., Pitterson, N. P., Dringenberg, E., & Ahn, B. (2024, June 23). Reimagining Behavioral Analysis in Engineering Education: A Theoretical Exploration of Reasoned Action Approach. 2024 ASEE Annual Conference & Exposition, Portland, Oregon.
Collaborative (In)decision: A Preliminary Investigation of the Differences in Undergraduate Engineering Capstone Students' Collaborative Behaviors
Published in International Journal of Engineering Education - Special Issue for Capstone Design, 2025
Recommended citation: Gerhardt, M., Madboly, M., Pitterson, N., Dringenberg, E., & Ahn, B. (2025). Collaborative (in)decision: A Preliminary Investigation of the Differences in Undergraduate Engineering Capstone Students’ Collaborative Behaviors. International Journal of Engineering Education, 41(4), 872–888.
Automated Analysis of Knowledge Types in Computer Science Textbooks: A Natural Language Processing Approach to Understanding Epistemic Climate
Published in In the proceedings of 2025 ASEE Annual Conference & Exposition, 2025
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Recommended citation: Gerhardt, M., & Katz, A. (2025). Automated Analysis of Knowledge Types in Computer Science Textbooks: A Natural Language Processing Approach to Understanding Epistemic Climate. 2025 ASEE Annual Conference & Exposition Proceedings, 55491. https://doi.org/10.18260/1-2--55491
Beyond Calculations: Engineering Judgment as Epistemic Cognition in Engineering Education
Published in In the proceedings of 2025 ASEE Annual Conference & Exposition, 2025
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Recommended citation: Gerhardt, M., Robinson, M., & Faulkner, B. (2025). Beyond Calculations: Engineering Judgment as Epistemic Cognition in Engineering Education. 2025 ASEE Annual Conference & Exposition Proceedings, 55504. https://doi.org/10.18260/1-2--55504
Inside the Mental Models: Instructor’s Conceptions on Generative Artificial Intelligence
Published in In the proceedings of 2026 ASEE STL Conference, 2026
Recommended citation: Shiekh, K. N., Chaback, B. E., Gerhardt, M., & Katz, A. (2026, April 24–25). Inside the Mental Models: Instructor’s Conceptions on Generative Artificial Intelligence. 2026 STL Annual Conference, Ithaca, NY.
It's like "X": How Engineering Faculty Metaphors Construct (and Constrain) GAI Understanding in Engineering Education
Published in In the proceedings of 2026 ASEE Annual Conference & Exposition, 2026
Recommended citation: Gerhardt, M., Shiekh, K., Katz, A., & Chaback, B. E. (2026, June). It's like "X": How Engineering Faculty Metaphors Construct (and Constrain) GAI Understanding in Engineering Education. 2026 ASEE Annual Conference & Exposition.
Understanding the Master’s Engineering Workforce Landscape: Employer Demands and Student Goals
Published in In the proceedings of 2026 ASEE Annual Conference & Exposition, 2026
Recommended citation: Clements III, H. R., Gerhardt, M., Sun, S., Budhathoki, R., Borrego, M., Deters, J., Katz, A., & Knight, D. (2026, June). RFE: Understanding the Master's Engineering Workforce Landscape: Employer Demands and Student Goals. 2026 ASEE Annual Conference & Exposition.
When Can GAI be Used Anyway? An Analysis of Engineering Faculty's Generative AI Policies
Published in In the proceedings of 2026 ASEE Annual Conference & Exposition, 2026
Recommended citation: Chaback, B. E., Katz, A., & Gerhardt, M. (2026). When Can AI be Used Anyway? An Analysis of Engineering Faculty’s Generative AI Policies. 2026 ASEE Annual Conference & Exposition Proceedings, 60771. https://doi.org/10.18260/1-2--60771
Improving Engineering Education GAI Qualitative Research Workflow Quality: Techniques and Documentation Strategies
Under review at Studies in Engineering Education - Special Issue on GAI in Methods
Recommended citation: Gerhardt, M., & Katz, A. (Submitted). Improving Engineering Education GAI Qualitative Research Workflow Quality: Techniques and Documentation Strategies. Studies in Engineering Education.
Mechanical Engineering Students’ Perspectives on Career Goals, Skills, and Industry
Forthcoming in In the proceedings of the 36th Australasian Association for Engineering Education Annual Conference, 2026
Recommended citation: Clements III, H. R., Gerhardt, M., Budhathoki, R., Deters, J., Borrego, M., Katz, A., & Knight, D. (2026, October). Mechanical Engineering Students' Perspectives on Career Goals, Skills, and Industry. 36th Australasian Association for Engineering Education Annual Conference.
What is Appropriate AI Use? Perceptions of Addictive AI Use Among STEM Graduate Student Coders
In preparation for Computers & Education: Artificial Intelligence
Recommended citation: Gerhardt, M., Katz, A., & Hooshangi, S. (In Preparation). What is Appropriate AI Use? Perceptions of Addictive AI Use Among STEM Graduate Student Coders. Computers & Education: Artificial Intelligence.
The Jagged Edge of Adoption: Comparing GenAI Adoption Between Coding and Non-Coding STEM Graduate Students
Under review at Symposium on Educational Advances in Artificial Intelligence (EAAI-27)
Recommended citation: Gerhardt, M., Hooshangi, S., Das, S., & Katz, A. (2027). (Submitted) The Jagged Edge of Adoption: Comparing GenAI Adoption Between Coding and Non-Coding STEM Graduate Students. Proceedings of the Symposium on Educational Advances in Artificial Intelligence (EAAI-27).
Refutable Reflexivity: A Theoretical and Visual Model for LLM-Infused Qualitative Research
Under review at International Journal of Qualitative Methods - Special Issue on Artificial Intelligence in the Analysis of Qualitative Data
Recommended citation: Gerhardt, M., Shiekh, K., Katz, A., & Chaback, B. E. (Submitted). Refutable Reflexivity: A Theoretical and Visual Model for LLM-Infused Qualitative Research. International Journal of Qualitative Methods.
Revisiting Transcription in the Age of AI: A Critical Review of Transcription in Qualitative Research
In preparation for International Journal of Qualitative Methods - Special Issue on Digital Transformation in Qualitative Research: Ethically Approaching New Methodological Horizons
Recommended citation: Gerhardt, M., Werth, A., & Kim, S. (In Preparation). Revisiting Transcription in the Age of AI: A Critical Review of Transcription in Qualitative Research. International Journal of Qualitative Methods.
What Should Graduate Engineering Students Learn About Generative AI? A Critical Review of GAI Competency in Engineering Graduate Education
In preparation for European Journal of Engineering Education
Recommended citation: Gerhardt, M., & Case, J. (In Preparation). What Should Graduate Engineering Students Learn About Generative AI? A Critical Review of GAI Competency in Engineering Graduate Education. European Journal of Engineering Education.
Like Mushrooms: Interdisciplinary Graduate Students Are the Fungi of Higher Education Ecosystems
In preparation for Studies in Graduate and Postdoctoral Education
Recommended citation: Gerhardt, M. (In Preparation). Like Mushrooms: Interdisciplinary Graduate Students Are the Fungi of Higher Education Ecosystems. Studies in Graduate and Postdoctoral Education.
WIP: The Challenge of Any Technology? Intergenerational Sense-Making About GenAI Systems in Engineering Education
Under review at In the proceedings of 2027 ASEE Annual Conference & Exposition
Recommended citation: Gerhardt, M., Shiekh, K., Chaback, B. E., & Katz, A. (2027). (Submitted) WIP: The Challenge of Any Technology? Intergenerational Sense-Making About GenAI Systems in Engineering Education. 2027 ASEE Annual Conference & Exposition, Toronto, Ontario, Canada.
WIP: Engineering Faculty Data Center Imaginaries: An Exploration of Pre-Pushback Perspectives of Internet Infrastructure
Under review at In the proceedings of 2027 ASEE Annual Conference & Exposition
Recommended citation: Li, N., Shiekh, K., Gerhardt, M., & Katz, A. (2027). (Submitted) WIP: Engineering Faculty Data Center Imaginaries: An Exploration of Pre-Pushback Perspectives of Internet Infrastructure. 2027 ASEE Annual Conference & Exposition, Toronto, Ontario, Canada.
Whose Innovation Is It? Bidirectional AI Sensemaking and Reconfigured Trust in an Engineering Internship Mentoring Program
Under review at In the proceedings of 2027 ASEE Annual Conference & Exposition
Recommended citation: Webb, M., & Gerhardt, M. (2027). (Submitted) Whose Innovation Is It? Bidirectional AI Sensemaking and Reconfigured Trust in an Engineering Internship Mentoring Program. 2027 ASEE Annual Conference & Exposition, Toronto, Ontario, Canada.
Decade-Long Analysis of Skills Across 1 Million Graduate-Level Job Advertisements
Published in Trends in Higher Education, 2026
Recommended citation: Gerhardt, M., Sun, S., Katz, A., Deters, J., Borrego, M., Knight, D., Clements III, H. R., & Budhathoki, R. (2026). Decade-Long Analysis of Skills Across 1 Million Graduate-Level Job Advertisements. Trends in Higher Education, 5(4), 119. https://doi.org/10.3390/higheredu5040119
Qualitatively Investigating Master’s Students’ GenAI Perceptions Using the Technology-to-Performance Model
In preparation for Education Sciences - Special Issue: Is AI a Double-Edged Sword? Perspectives from Teachers and Students
Prepared for the Education Sciences special issue (submission deadline: 31 October 2026).
Recommended citation: Wright, C., Gerhardt, M., Clements III, H. R., Borrego, M., Knight, D., Deters, J., & Katz, A. (In Preparation). Qualitatively Investigating Master's Students' GenAI Perceptions Using the Technology-to-Performance Model. Education Sciences.
Leveraging Large Language Models in Engineering Education Research: Methods and Applications
Forthcoming in 2027 International Handbook of Engineering Education Research Methods, 2027
Recommended citation: Gerhardt, M., Coloyan Fleming, G., Wei, S., & Katz, A. (2027). (In Press) Leveraging Large Language Models in Engineering Education Research: Methods and Applications. 2027 International Handbook of Engineering Education Research Methods.
The Problem With Prompting: Using Decision Models for Traceable and Justifiable GenAI-Infused Qualitative Data Analysis
In preparation for Organizational Research Methods
Recommended citation: Gerhardt, M., & Katz, A. (In Preparation). The Problem With Prompting: Using Decision Models for Traceable and Justifiable GenAI-Infused Qualitative Data Analysis. Organizational Research Methods.
talks
Engineering Industry Practices and Career Pathways
Published:
Discussion of engineering industry workplace practices and career pathways with capstone students.
Reimagining Behavioral Analysis in Engineering Education: A Theoretical Exploration of Reasoned Action Approach
Published:
Examining decision-making in engineering student workplace teams through the lens of the Reasoned Action Approach.
Teamwork Dynamics and Collaborative Practices in Capstone Engineering
Published:
Panel discussion examining teamwork dynamics and collaborative practices in capstone engineering workplace contexts.
Why do capstone students choose to perform behaviors? Differing prevalence in collaborative choices
Published:
Poster presentation examining the differing prevalence of collaborative behavioral choices among capstone engineering students.
GAI Adoption in Higher Education
Published:
Discussed research on GenAI adoption in higher education and ongoing projects examining workplace technology integration with university leadership.
Automated Analysis of Knowledge Types in Computer Science Textbooks: A Natural Language Processing Approach to Understanding Epistemic Climate
Published:
Exploring the use of LLMs to evaluate types of knowledge described in computer science textbooks.
Beyond Calculations: Engineering Judgment as Epistemic Cognition in Engineering Education
Published:
Arguing for greater psychological interrogation of “engineering judgment” as a form of epistemic cognition in engineering education.
Large Language Models and Sociotechnical Applications in Engineering Education Research
Published:
Presented technical background of large language models, AI history, and sociotechnical applications in engineering education research. Demonstrated how computational methods intersect with critical perspectives on workplace learning and expertise.
Large Language Models in Qualitative Engineering Education Research: Technical Methods and Methodological Considerations
Published:
Examined human vs. machine pattern recognition, current GenAI approaches for qualitative research, researcher positionality, translational challenges, and shifting definitions of “intelligence.” Addressed methodological questions about computational approaches to qualitative work and implications for research practice.
AI Research Ethics in Engineering Education
Published:
Co-presented with Andrew Katz to ECE graduate students examining ethical dimensions of AI research in educational contexts.
Scholarly Ethics
Published:
Presentation to incoming GTAs at Virginia Tech, addressing topics like academic and professional integrity, the VT Undergraduate and Graduate Honor Systems, GTA integrity responsibilities, GenAI use, and contemporary literature about cheating.
Workshop on GAI Course Policies for Virginia Tech Graduate Instructors
Published:
Initiated and led a three-student group to organize a workshop for Virginia Tech graduate instructors about GenAI course policies. Addressing a critical need through participatory instruction and activities that promote GenAI literacy. Received IRB approval to study the workshop’s implementation and effectiveness, with plans to publish results. Leveraging networks and existing partnerships with the Graduate Honor System (GHS), the Center for Excellence in Teaching and Learning (CETL), the center for Technology-Enhanced Learning and Online Strategies (TLOS), and departmental leadership to host and market the event.
Impact of Generative AI Applications on Current Doctoral Practices and Implications for the Future
Published:
Special session critically examining the use of GenAI within doctoral training and education. In addition to expert opinions, it gave participants the opportunity to contribute to ongoing discussion on the topic and provide guidance and feedback on what basic guidelines for use of GenAI within doctoral education should look like.
It's like "X": How Engineering Faculty Metaphors Construct (and Constrain) GAI Understanding in Engineering Education
Published:
Presenting work examining the figurative language used to describe “AI” from engineering instructors from Spring 2025. Findings revealed wide variety in conceptualizations of AI, raising the imperative for faculty developers to work as “translators” between meaning-making systems.
Responsible GenAI Use: GenAI, Course Policy, and Academic Integrity in ALCE
Published:
Invited presentation at a department meeting of the Department of Agricultural, Leadership, and Community Education, with roughly 30 faculty and graduate students. The session focused on GenAI course policy and academic integrity, treating “responsible” use as a question about disciplinary norms — what ALCE scholarship values in writing, analysis, and community engagement — rather than a blanket rule. Connects to ongoing work on GenAI course policies and graduate instructor development at Virginia Tech. Attendee feedback: “We all found the session very helpful.”
Are We Talking About the Same Summer? Mentor-Mentee Perception Alignment and the Question of AI in Engineering Internships
Published:
Presented with Dr. Maggie Webb (Webb, M., & Gerhardt, M.). Work with a Rice University bridge program examining how students recognized differences between how practitioners and instructors orient toward AI in engineering internships, and how hard that dissonance is to negotiate.
Large Language Models in Qualitative Engineering Education Research: Technical Methods and Methodological Considerations
Published:
Return engagement with the fall 2026 cohort of Dr. Borrego’s qualitative research methods course. Examined human vs. machine pattern recognition, current GenAI approaches for qualitative research, researcher positionality, translational challenges, and shifting definitions of “intelligence.” Addressed methodological questions about computational approaches to qualitative work and implications for research practice.
teaching
Fulbright Brazil Workshop: Innovating Engineering Education with Generative AI
Workshop, INOVA.USP, Campus da Cidade Universitária, 2025
I assisted my advisor, Dr. Andrew Katz, along with Dr. Dayoung Kim, in delivering a three-day workshop hosted by Fulbright Brazil exploring how generative AI can transform engineering education. The workshop brought together university educators, industry experts, and thought leaders from across Brazil and beyond.
Fulbright Brazil Workshop (Part 2): Getting Dirty with Generative AI
Workshop, Virginia Tech Arlington Campus, 2026
The second half of a two-part workshop with Fulbright Brazil cultivating AI literacy among Brazilian engineering faculty. Where the São Paulo session presented foundational content on AI systems, pedagogy, ethics, and higher education governance, this session “got our hands dirty” with the same material. Put another way, trading lecture-style teaching and short breakouts for prolonged discussions and collaborations so participants could identify actionable, specific AI supports for their own teaching and institutions.