Our Mission

The IDEEAS Lab develops AI-driven methodologies to transform decision-making processes in engineering education and practice.

Research Thrusts

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AI-Powered Pedagogy

Developing intelligent tutoring systems and NLP tools for engineering education

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Decision Architectures

Modeling complex decision-making processes in engineering systems

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Cognitive Modeling

Studying mental models of AI in educational contexts

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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

National Science Foundation CAREER Award$592,000

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

National Science Foundation Engineering Education Centers$840,737

2023-2027

Modeling social-ecological systems in engineering design decisions

EAGER: Natural Language Processing for Teaching and Research in Engineering Education (NLPTREE)

National Science Foundation EAGER$299,647

2022-2025

Developing NLP pipelines for engineering education research

Using Large Language Models and Generative AI to Scale Qualitative Data Analysis

Virginia Tech Academy of Data Science Discovery Fund$10,000

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

Virginia Tech +Policy Fellowship$10,000

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

National Science Foundation$174,616

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

NBME Stemmler Award$136,296

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

Seed Project (Unfunded)Unfunded

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

Seed Project (Unfunded)Unfunded

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

Seed Project (Unfunded)Unfunded

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

Seed Project (Unfunded)Unfunded

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

Seed Project (Unfunded)Unfunded

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

Seed Project (Unfunded)Unfunded

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.