Talks
-
Governing Generative AI in Higher Education: Patterns in University AI Policies Across R1 and R2 Institutions
This talk presents a large-scale analysis of university generative AI policies using a two-stage workflow that combines structured ADICO extraction with inductive qualitative coding. Drawing on 367 policy documents across 226 R1 and R2 institutions, the presentation examines what actions universities govern, how restrictive or permissive those policies are, and how patterns vary by institutional characteristics such as sector and classification. The session also highlights the methodological contribution of using the GATOS workflow to move from structured rule extraction to bottom-up thematic analysis at scale.
-
Design Decisions for AI-Assisted Qualitative Data Analysis
This workshop walks through practical design decisions when building LLM-based workflows for qualitative data analysis. Grounded in Braun & Clarke's thematic analysis framework, the session covers key choices including: workflow structure mapping to established methods, data familiarization through LLM summarization, clustering approaches using embeddings and dimensionality reduction, RAG-based code generation strategies, theme identification techniques, and critical human-in-the-loop validation points. Drawing on examples from published research using the GATOS (Generative AI-enabled Theme Organization and Structuring) workflow, participants will learn prompt engineering strategies for educational contexts, approaches for maintaining parsimony in AI-generated codebooks, and validation techniques including Cohen's κ comparison and cosine similarity analysis. The workshop emphasizes using open-source models for privacy and reproducibility while maintaining rigorous qualitative research standards.
-
From Natural Language to New Understandings: Developing AI-Powered Methodologies for Engineering Education Research and Practice
The integration of artificial intelligence and natural language processing (NLP) into engineering education research and practice represents a set of opportunities to expand how we understand student learning, assess educational outcomes, and scale qualitative research methods. This talk presents a comprehensive overview of methodological innovations developed in the IDEEAS Lab over the past five years to leverage the power of generative AI, large language models, and NLP techniques for advancing engineering education. I will present three interconnected methodological contributions: (1) AI-assisted qualitative analysis, where I demonstrate how generative text models can perform traditional thematic data analysis while maintaining rigor and validity; (2) AI-assisted assessment and feedback systems, showcasing applications of open-source language models for analyzing student responses, teamwork feedback, and ethics case studies; and (3) mental models research at scale, illustrating how NLP techniques can help uncover engineering faculty and student mental models of complex topics like sustainability, assessment, and now generative AI itself. Through concrete examples from NSF-funded research projects, I will share methodological lessons learned from analyzing thousands of student responses, faculty interviews, and educational artifacts using AI tools. Key methodological insights include: strategies for prompt engineering in educational contexts, approaches for validating AI-generated qualitative codes against human coding, and frameworks for maintaining ethical standards when using AI in educational research. The talk will conclude with a forward-looking discussion of emerging opportunities and challenges, including strategies for development and dissemination of open-source AI tools for education researchers, the implications of AI for traditional qualitative research paradigms, and how these methodological advances can inform evidence-based improvements to engineering curricula and pedagogy. This work represents a new frontier in engineering education research methodology. The ultimate vision is to leverage AI not to replace human insight, but to amplify our capacity to understand and improve engineering education at unprecedented scale and depth.
-
How to Think About Applying Large Language Models to Support Engineering Education Research
There are many ways researchers and practitioners are applying large language models (LLMs) to support engineering education research. Such integrations of AI and natural language processing (NLP) into engineering education research present a set of opportunities to expand how we understand processes in education contexts and scale qualitative research methods. This seminar will present examples of applying LLMs and NLP techniques for advancing engineering education across a range of domains, data types, and research questions. I will present studies where LLMs were utilized to support data cleaning and analysis so that we can identify takeaways and methodological observations across those studies. The goal is to provide attendees with ideas and strategies for where they may want to apply (or avoid!) LLMs in their own research. Example studies will include applications of LLMs to study use of figurative language, student career pathway decisions, and engineers' design ideation. The talk will conclude with a discussion of general strategies and considerations for applying LLMs in engineering education research.
-
Engineering Education Research Methods Workshop
This workshop focuses on building aligned, rigorous study designs in engineering education research. Participants will explore key concepts including research paradigms, theoretical frameworks, research questions, and methodological approaches. The session emphasizes the importance of alignment between research questions, theoretical perspectives, and methods, while providing practical guidance on designing robust studies. Topics include qualitative, quantitative, and mixed methods approaches, validity and reliability considerations, and strategies for developing coherent research proposals.
-
Lessons from Applying Large Language Models in Engineering Education Research
The integration of artificial intelligence and natural language processing (NLP) into engineering education research and practice represents a set of opportunities to expand how we understand processes in education contexts and scale qualitative research methods. This talk presents examples and lessons learned from applying large language models (LLMs) and NLP techniques for advancing engineering education across a range of study contexts and data types. I will present studies where LLMs were utilized in various stages of data collection and analysis with an eye toward synthesizing methodological observations across those studies. The goal is to help seminar attendees glean insights and ideas to inform their own engagement with LLMs. Examples will include: strategies for prompt engineering in educational contexts, approaches for validating AI-generated qualitative codes against human coding, and strategies for study design. The talk will conclude with a discussion of emerging opportunities and challenges, including strategies for development and dissemination of open-source AI tools for education researchers, the implications of AI for traditional qualitative research paradigms, and how these methodological advances can inform evidence-based improvements to engineering curricula and pedagogy.
-
Seminar on Generative AI in Engineering Education for Analyzing Qualitative Data
Invited seminar covering emerging uses of generative AI in engineering education, research workflows, and responsible integration.
-
Workshop: Assessment Strategies in Engineering in the Age of Generative AI
Pre‑workshop lecture focused on designing meaningful assessments in the AI era, with practical patterns and pitfalls.
-
Integrating AI into Engineering Education (Workshop)
Workshop focused on strategies for integrating generative AI into engineering education, with a focus on content, assessment and pedagogy.
-
Panel: Environmental Engineering Education and AI
Panel discussion on opportunities, risks, and responsible practices when deploying AI in environmental engineering education settings.
-
Panel: Ethics, Professional Practice, and AI
Panel on ethical, professional, and practical considerations for AI in higher education and research.
-
Strategies for Integrating Generative AI in Engineering Education
Conference presentations on AI in engineering education including frameworks, case studies, and instructional practices.
-
Seminar on AI in Engineering Education
Department seminar discussing state-of-practice for AI adoption in engineering curricula and research.
-
Strategies for Integrating Generative AI in Engineering Education
Conference presentations on AI in engineering education including early results, design patterns, and policy implications.
-
Seminar on AI-Enabled Learning Experiences
Seminar focused on designing repeatable AI-enabled learning activities, with examples from multiple disciplines.
-
Seminar on Generative AI for Research and Instruction
Seminar addressing practical generative AI use for research workflows and teaching strategies.