Work with us
Researchers, organizations, students, and anyone following the work: each path below begins with an email.
Academic collaborators
Collaboration inquiries are most productive when they name a decision context, dataset or field site, timeline, and intended scholarly output.
- Send
- Draft aims, study protocol, dataset description, proposal idea, or publication plan.
- Timing
- Best before study design or data collection decisions are locked.
Sponsored and collaborative research
Faculty at other institutions, research centers, and agencies planning a study or a proposal.
Questions we hear
- “Can we answer this question at a scale that manual coding cannot reach?”
- “What would a defensible mixed-methods design look like here?”
- “Who should be on this proposal, and what would our part of it be?”
What you get
- Co-authored proposals and manuscripts
- Study designs, instruments, and analysis plans
- Shared data and method documentation
- How long
- Months to years. Usually begins before a proposal deadline, not after an award.
- What it asks of you
- Proposal work needs lead time. Reaching out a week before a deadline rarely produces a good submission.
Industry and organizations
We take on work where a hard question meets data nobody can read by hand, and we say up front what the method can and cannot settle.
Applied research and development
Organizations holding text, survey, or interview data they cannot analyze at scale in-house.
Questions we hear
- “What are the themes across ten thousand open-ended responses?”
- “How do we turn an analysis we ran once into a workflow we can repeat?”
- “Is a decision-support tool the right answer here, or would a study serve us better?”
What you get
- Scoped analyses with documented method and limitations
- Reproducible workflows your team can run
- Prototype tools and evaluation evidence
- How long
- Weeks to months, usually a scoped pilot before anything larger.
- What it asks of you
- Engagements run through Virginia Tech, not as private consulting. Data-sharing and IRB review take time and must be settled up front.
Workshops and outreach
Teaching and learning centers, departments, conferences, and professional societies.
Questions we hear
- “How should our faculty actually use generative AI in teaching and assessment?”
- “Can you train our researchers in AI-assisted qualitative methods?”
- “Will you speak to our group about what this technology does and does not do well?”
What you get
- Hands-on workshops and faculty development sessions
- Methods training for research teams
- Talks, panels, and briefings
- How long
- A single session to a multi-day series. The shortest path to working together.
- What it asks of you
- Scheduling generally needs a month or more of notice, and travel costs may apply.
How an engagement runs
Every engagement starts small on purpose.
- Scoping call. Clarify the question, the decision it feeds, the constraints, and what a useful answer would look like.
- Feasibility and data review. Assess the data you actually have, whether the method fits, and what approvals the work requires.
- Pilot and evidence plan. Agree a small first pass with explicit success criteria, so both sides can stop early if it is not working.
- Delivery and handover. Deliver the analysis, the documentation, and enough method detail that your team can run or extend it.
Graduate students
We are taking new graduate students for the next admissions cycle. If your interests connect to the lab's research, start with a conversation about fit.
Graduate admission runs through the Department of Engineering Education at Virginia Tech. Write first with your interests and a paper or project of ours you would build on.
A strong first note names the questions you want to study, why this lab fits them, and how your earlier work prepares you for independent research.
- Send
- CV, research interests, one or two connected papers or projects, and an optional writing or code sample.
- Timing
- Best before department application deadlines.
Email Andrew about graduate study
Before you write
- Name your path: collaborator, organization, graduate student, undergraduate, or something else.
- Connect your interests to a project, paper, or research theme.
- Attach a CV or resume and one relevant sample of your work when you have one.
- Include timeline, weekly availability, funding needs, or application cycle.
After you email
- We check fit with current projects, advising capacity, and timelines.
- If there is a match, we schedule a conversation or suggest a project contact point.
- For student roles, follow-up may include a small task, writing sample, or project meeting.
Papers to read first
- Using generative AI for large-scale qualitative analysis of social media posts to understand why people leave computer science
- Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development
- How do engineering faculty understand generative AI, and how do those mental models shape instructional decisions?
Undergraduate researchers
Undergraduate RAs usually join through project-specific roles with concrete tasks, expected skills, and semester-scale commitments.
- Send
- Resume, weekly availability, relevant coursework, preferred project areas, and an optional portfolio link.
- Timing
- Best a few weeks before a new semester starts.
Talks, media, and the public
We share what we find as widely as we can: plain-language findings with the figures and citations behind them, open methods, and talks for audiences inside and outside academia.
- Findings, one result from a published paper at a time, written for anyone curious. Follow new findings by RSS.
- Talks the lab has given, with slides where they exist.
- For a talk, a panel, or a media inquiry, email Andrew Katz with the audience, the date, and the question you would like covered.
Our track record
The lab's work is supported by the National Science Foundation (including a CAREER award, an Engineering Education Centers award, and an EAGER award) and by the National Board of Medical Examiners through a Stemmler Award. Methods developed in this work are published in venues including the Journal of Engineering Education and Humanities and Social Sciences Communications.
The research in cross-sectionthe body of work as it accumulated, with funded work ahead
Scroll sideways for earlier years.
71 papers so far
Funded work runs through 2029
Most recent paper: Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains (2026)
Read left to right through time. Like sediment, each area's layer only grows: its thickness is the papers published there so far (a paper spanning two areas counts half in each). Dots mark each paper in the year it appeared. Above the surface: funded projects and how far ahead they run.
Every paper in this cross-section, by area
The field and its institutions
- Curriculum comparison: chemical and mechanical engineering education in the United States and Turkey (2025)
- Advanced Considerations in Quantitative Methods for New Directions in Engineering Education Research (2023)
- Analysis of Advances in Engineering Education Publications (2007-2020) to Examine Impact and Coverage of Topics (2022)
- Applying concepts from political science and economics to advance the study of engineering education (2022)
- Factors associated with collaboration networks in ASEE conference papers (2021)
- Taking stock: An Analysis of IJEE publications from 1996--2020 to examine impact and coverage of topics (2021)
- Learning from failures: Engineering education in an age of academic capitalism (2018)
- The revealing effects of disaster: A case study from Tulane University (2016)
Ethics, equity, and responsibility
- How do ethics and diversity, equity, and inclusion relate in engineering? A systematic review (2024)
- Collateral Damage: Investigating the Impacts of COVID on STEM Professionals with Caregiving Responsibilities (2022)
- Using Natural Language Processing to Explore Undergraduate Students’ Perspectives of Social Class, Gender, and Race (2022)
- Engineering ethics in engineering design courses: A preliminary investigation (2021)
- The correlation between undergraduate student diversity and the representation of women of color faculty in engineering (2021)
- Your views can be my views: Understanding differences in paradigms held by traditionally marginalized students in engineering (2021)
- An Investigation of When and Where Ethics Appears in Undergraduate Engineering Curricula (2020)
- Overcoming Challenges to Enhance a First Year Engineering Ethics Curriculum (2020)
- Monetizing Life May Be the Ethical Thing to Do (2019)
- Investigating influences on first-year engineering students’ views of ethics and social responsibility (2018)
- Factors related to faculty views of undergraduate engineering ethics education (2017)
- Telling it like it was: Histories of change in engineering ethics education (2017)
- Shadow codes of engineering ethics: An experiment in ethics imaginaries (2016)
- Educating for a revolution: Discerning together the Highlander idea and its lessons for ESJP’s work (2015)
How faculty think, teach, and assess
- Understanding instructor decision-making in engineering education for sustainable development: a comparison of institutions in Denmark and the United States (2025)
- Paradigm Shift? Preliminary Findings of Engineering Faculty Members’ Mental Models of Assessment in the Era of Generative AI (2024)
- Exploring Faculty Members' Conceptualizations of Diversity, Equity, and Inclusion in Engineering Education (2023)
- Promoting Research Quality to Study Mental Models of Ethics and Diversity, Equity, and Inclusion (DEI) in Engineering (2023)
- WIP: Faculty Use of Metaphors When Discussing Assessment (2023)
- Defining Assessment: Foundation Knowledge Toward Exploring Engineering Faculty’s Assessment Mental Models (2022)
How students learn and experience engineering
- Exploring the Impact of Engineering Projects in Community Service on Engineering Students’ Perspectives about Engineering as a Major (2023)
- How Participating in Extracurricular Activities Supports Dimensions of Student Wellness (2023)
- Development of hybrid laboratory sessions during the COVID-19 Pandemic (2022)
- Students’ Feedback About Their Experiences in EPICS Using Natural Language Processing (2022)
- Understanding First-year Engineering Students’ Perceptions of Working with Real Stakeholders on a Design Project: A PBL Approach (2022)
- Using Sentiment Analysis to Evaluate First-year Engineering Students Teamwork Textual Feedback (2022)
- Harvesting tweets for a better understanding of engineering students' first-year experiences (2020)
- Using Chatbots as Smart Teaching Assistants for First-Year Engineering Students (2020)
Sustainability and climate
- Inspiring Sustainability in Undergraduate Engineering Programs (2024)
- A Thematic and Trend Analysis of Engineering Education for Sustainable Development (2022)
- Augmented Reality for Sustainable Collaborative Design (2022)
- Civil Engineering Students’ Beliefs about Global Warming and Misconceptions about Climate Science (2021)
- Higher perceived design thinking traits and active learning in design courses motivate engineering students to tackle energy sustainability in their careers (2021)
- Predicting engineering students’ desire to address climate change in their careers: An exploratory study using responses from a U.S. National survey (2021)
- Civil Engineering Students’ Beliefs about the Technical and Social Implications of Global Warming and When Global Warming Will Impact Them Personally and Others (2020)
Careers and the workforce
- Using generative AI for large-scale qualitative analysis of social media posts to understand why people leave computer science (2025), read the finding
- Engineering students' interests in nonprofit and public policy careers: Applying a data-driven approach to identifying contributing factors (2025), read the finding
- Skill Development of Engineering and Physical Science Doctoral Students: Understanding the Role of Advisor, Faculty, and Peer Interactions (2024)
- What engineering employers want: An analysis of technical and professional skills in engineering job advertisements (2024)
- An Empirical Study of Programming Languages Specified in Engineering Job Postings (2022)
Technology policy
Language technology for education research
- Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains (2026)
- Automated Analysis of Knowledge Types in Computer Science Textbooks: A Natural Language Processing Approach to Understanding Epistemic Climate (2025)
- A Reinforcement Learning Framework for N-Ary Document-Level Relation Extraction (2024)
- Exploring NLP-based Methods for Generating Engineering Ethics Assessment Qualitative Codebooks (2023)
- Pushing Ethics Assessment Forward in Engineering: NLP-Assisted Qualitative Coding of Student Responses (2023)
- Utilizing Natural Language Processing to Examine Self-Reflections in Self-Regulated Learning (2023)
- Clustering-based unsupervised generative relation extraction (2022)
- Work-in-Progress: Using Latent Dirichlet Allocation to uncover themes in student comments from peer evaluations of teamwork (2022)
- Figurative language in computer education: Evidence from YouTube instructional videos (2021)
- Using natural language processing to facilitate student feedback analysis (2021)
- Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction (2020)
Generative AI for qualitative research
- Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development (2026), read the finding
- Advancing Qualitative Analysis in Professional Disaster and Risk Communication: A Comparative Study of an OpenAI ChatGPT 3.5 Model-Enabled Method for Processing Complex Public Posts (2025)
- Expanding possibilities for generative AI in qualitative analysis: Fostering student feedback literacy through the application of a feedback quality rubric (2025), read the finding
- Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis (2025)
- From Manual Coding to Machine Understanding: Students' Feedback Analysis (2024)
- Novel Approach Designing Interview Protocols with Generative Large Language Models to Study Mental Models and Engineering Design (2024)
- Stumbling Our Way Through Finding a Better Prompt: Using GPT-4 to Analyze Engineering Faculty’s Mental Models of Assessment (2024)
- Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching (2024)
- Advancing qualitative analysis: An exploration of the potential of generative AI and NLP in thematic coding (2023)
- Exploring the Efficacy of ChatGPT in Analyzing Student Teamwork Feedback with an Existing Taxonomy (2023)
- The Utility of Large Language Models and Generative AI for Education Research (2023)
Every award, amount, and duration is listed under Research, and the methods behind them are under Tools. We would rather you check the record than take our word for it.
Contact
Andrew Katz
Associate Professor, Engineering Education
Virginia Tech
[email protected]
Meeting location and availability vary by term and are shared after an initial fit check.
We aim to respond within about five business days. During travel, deadlines, or admissions-heavy periods, response time can be longer.