Research
Research at the IDEEAS Lab
We develop AI-driven methods and human-centered tools for transparent, equitable decision-making in engineering education and practice, and we keep human judgment central to every workflow.
The Program
Methods, evidence, and tools
Our work spans natural language processing, qualitative methods, and the learning sciences. We build AI-assisted research workflows that scale analysis without hiding the decisions behind it. The results stay inspectable, accountable, and useful to the people they describe.
This page is the front door to that work: the questions we pursue, the projects funding it, the papers reporting it, and the talks where we share it. The tools we build along the way live in Studio.
Focus Areas
Where we concentrate
Mental Models
Exploring how people conceptualize systems, decisions, and AI tools.
02Sustainable Design
Modeling social-ecological systems to inform responsible engineering decisions.
03Large-Scale Qual Data Analysis
LLM-enabled workflows that scale qualitative coding and sensemaking.
04AI in Education
AI tools that support teaching, learning, and feedback in engineering education.
05Workforce & Labor Markets
Understanding skills, roles, and trends shaping engineering careers.
06Decision Support & Policy
Evidence-based tools for policy analysis and complex decision support.
Projects
Active research projects
CAREER: Minds and Machines: Exploring Engineering Faculty Member Mental Models of Generative AI and Instructional Decisions
Investigating faculty mental models of generative AI in engineering education
Design for Sustainability: How Mental Models of Social-Ecological Systems Shape Engineering Design Decisions
Modeling social-ecological systems in engineering design decisions
EAGER: Natural Language Processing for Teaching and Research in Engineering Education (NLPTREE)
Developing NLP pipelines for engineering education research
Using Large Language Models and Generative AI to Scale Qualitative Data Analysis
Leveraging open-source large language models and generative AI to create workflows to conduct large-scale qualitative data analysis
Publications
Recent publications
- Thematic analysis with open-source generative AI and machine learning: A new method for inductive qualitative codebook development
Andrew Katz, Gabriella Coloyan Fleming, Joyce B. Main · Humanities and Social Sciences Communications · 2026
- Using generative AI for large-scale qualitative analysis of social media posts to understand why people leave computer science
Amanda Ross, Andrew Katz · Journal of Engineering Education · 2025
- 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
Margaret Webb, Harman Singh, Rachel Inman, Sweta Baniya, et al. · International Journal of Disaster Risk Reduction · 2025
- Expanding possibilities for generative AI in qualitative analysis: Fostering student feedback literacy through the application of a feedback quality rubric
Katherine Drinkwater Gregg, Olivia Ryan, Andrew Katz, Mark Huerta, et al. · Journal of Engineering Education · 2025
- Automated Analysis of Knowledge Types in Computer Science Textbooks: A Natural Language Processing Approach to Understanding Epistemic Climate
Mitchell Gerhardt, Andrew Katz · Proceedings of the 2025 ASEE Annual Conference & Exposition · 2025
Talks
Recent talks
- ISCE 2026
Governing Generative AI in Higher Education: Patterns in University AI Policies Across R1 and R2 Institutions
Talk - SEFI 2026 (Virtual)
Design Decisions for AI-Assisted Qualitative Data Analysis
Workshop - Purdue University
From Natural Language to New Understandings: Developing AI-Powered Methodologies for Engineering Education Research and Practice
Seminar
Work with us
We collaborate with researchers, instructors, and partners on datasets, methods, and deployable tools, and we recruit students who want to build real research infrastructure.