Research Collaborations
We work with faculty, research teams, and students on rigorous AI-enabled research in engineering education and decision support.
Collaboration Areas
Where we can contribute immediately
Large-Scale Qual Data Analysis
Scale qualitative coding and sensemaking with reproducible LLM-enabled workflows.
- Interviews and focus groups
- Survey free-response analysis
- Social media discourse analysis
AI in Education
Design and evaluate AI-supported instruction, feedback systems, and learning tools.
- Course-integrated AI activities
- Assessment and rubric design
- Student-facing AI tool studies
Mental Models
Map how people understand systems, risks, and AI, then connect those models to decisions.
- Concept map analysis
- Belief and adoption studies
- Expert-novice comparisons
Sustainable Design
Model social-ecological systems to support equitable and sustainable engineering choices.
- Tradeoff analysis
- Policy-sensitive design scenarios
- Complex systems visualization
Workforce and Labor Markets
Use computational methods to characterize skill demand, pathways, and workforce trends.
- Job posting analysis
- Skill taxonomy development
- Education-to-work transitions
Decision Support and Policy
Build evidence-based decision support tools and policy analyses for complex domains.
- Policy document analysis
- Stakeholder-facing dashboards
- Decision framework prototyping
Engagement Models
Academic and lab engagement formats
Academic Research Collaborations
Collaborate on studies, co-authored manuscripts, and conference submissions across institutions.
Funded Grant Partnerships
Co-develop proposals and execute multi-institution funded projects with shared deliverables.
Research Assistant Pathways
Prospective undergraduate and graduate researchers can join active projects through structured onboarding.
For Students
Join as a research assistant
We regularly involve undergraduate and graduate research assistants in funded and seed projects. Typical roles include qualitative coding, NLP and data analysis workflows, literature synthesis, and co-development of manuscripts and presentations.
Process
How we start collaborations
STEP 1
Scoping Call
Clarify the decision context, key questions, constraints, and expected outputs.
STEP 2
Feasibility and Data Review
Assess available data, methodological fit, timeline, and resource requirements.
STEP 3
Pilot and Evidence Plan
Define a pilot workflow, evaluation criteria, and success metrics.
STEP 4
Execution and Knowledge Transfer
Deliver analysis artifacts, implementation guidance, and reusable documentation.
Start a collaboration conversation
Send a short brief with your research context, available data, and timeline. We can recommend an appropriate path for collaboration, co-authorship, grant development, or student involvement.