Our Mission
The IDEEAS Lab develops AI-driven methodologies to transform decision-making processes in engineering education and practice.
Research Thrusts
AI-Powered Pedagogy
Developing intelligent tutoring systems and NLP tools for engineering education
Decision Architectures
Modeling complex decision-making processes in engineering systems
Cognitive Modeling
Studying mental models of AI in educational contexts
Sustainable Systems
Optimizing resource allocation through machine learning
Funded Projects
CAREER: Minds and Machines: Exploring Engineering Faculty Member Mental Models of Generative AI and Instructional Decisions
2024-2028
Investigating faculty mental models of generative AI in engineering education
Design for Sustainability: How Mental Models of Social-Ecological Systems Shape Engineering Design Decisions
2023-2027
Modeling social-ecological systems in engineering design decisions
EAGER: Natural Language Processing for Teaching and Research in Engineering Education (NLPTREE)
2022-2025
Developing NLP pipelines for engineering education research
Using Large Language Models and Generative AI to Scale Qualitative Data Analysis
2024-2025
Leveraging open-source large language models and generative AI to create workflows to conduct large-scale qualitative data analysis
Structures and Machines: An Interdisciplinary Approach to Mapping the Policy Implications of Generative AI in Higher Education
2024-2025
Analyzing existing policies related to AI in higher education across the US
The Engineering Master's Workforce: Leveraging Natural Language Processing Techniques to Understand Employer Demands and Student Goals
September 2024 - August 2027
Studying labor market trends by analyzing job postings with natural language processing to understand employer demands and how they align with engineering master's student goals.
Operationalizing, Validating, and Scaling Health Systems Citizenship Assessment in Undergraduate Medical Education
2023-2025
Developing NLP and AI-based tools to support assessment of health systems citizenship and to characterize medical students' mental models of health systems.
Exploring student perceptions of generative AI expressed on social media
2025–2026
Large-scale qualitative analysis of opinions and perspectives about generative AI expressed on social media (e.g., Reddit). We will use NLP and LLM-assisted workflows to sample, code, and synthesize themes across communities over time.
A Scalable Approach to Policy Analysis
2025–2026
Use NLP and LLM-based techniques to analyze policy documents and legislation from state legislature sources (e.g., the National Conference of State Legislatures website), with a focus on workforce development, environmental, and energy policy.
Figurative Language in Educational Contexts
2025–2026
Analyze transcripts from course recordings (e.g., YouTube) to identify figurative language such as metaphors and analogies used by instructors when explaining difficult or abstract concepts. Use NLP and LLMs to facilitate identification and classification.
Scaffolding Student Self-Regulated Learning with Large Language Models
2025–2026
Use LLMs and self-regulated learning (SRL) theory to build a full-stack web app that scaffolds students across forethought, performance, and self-reflection phases (e.g., goal-setting, planning, monitoring, reflection).
Characterizing Design Decisions with LLMs
2025–2026
Use LLMs to identify design decisions and the factors that influence them from qualitative text (e.g., interviews, design notebooks, reports). One outcome is constructing causal influence diagrams that represent decisions and relationships.
Using Vision Language Models to Analyze Concept Maps
2025–2026
Use vision language models (VLMs) to analyze concept maps and other visualizations of complex systems. One application is to study how students represent and reason about social-ecological systems in engineering design.