2026 ASEE Annual Conference & Exposition

LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations

Presented at The Intersection of AI and Methods for Research and Teaching

This full research paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitative research. As LLMs become increasingly integrated into scholarly workflows, qualitative researchers face unique challenges related to interpretation, reflexivity, trustworthiness, and ethical data use. Rather than evaluating or comparing specific models, this paper adopts a model-agnostic perspective and focuses on features common across contemporary LLMs, including context window limitations, stochastic generation and temperature settings, system prompts, and system-level safeguards.

Overall, this paper provides a practical primer intended to help qualitative researchers critically assess when, how, and whether LLMs may be appropriately integrated into their research workflows. By highlighting both opportunities and limitations at the feature level, we aim to support more deliberate, transparent, and responsible uses of LLMs in qualitative inquiry

Authors

Mr. Henry Salgado http://orcid.org/https://0009-0001-6506-5484 University of Texas at El Paso [biography]

Henry is a Ph.D. candidate (ABD) in Computer Science whose research focuses on explainable AI, particularly through modeling approaches such as causal inference. His work explores how machine learning models can be made more interpretable and scientifically useful. He also studies the science of science, with interests in faculty roles in education, student persistence in engineering, and the impact of institutional contexts on these outcomes.
Dr. Meagan R. Kendall http://orcid.org/0000-0002-9940-4405 University of Texas at El Paso [biography]

An Associate Professor at The University of Texas at El Paso, Dr. Meagan R. Kendall is a founding member of the Department of Engineering Education and Leadership. With a background in both engineering education and design thinking, her research focuses on how Latinx students develop identities as engineers and navigate moments of identity interference, student and faculty engineering leadership development through the Contextual Engineering Leadership Development framework, and promoting student motivation. Dr. Kendall is the Past Chair of the Engineering Leadership Development Division of ASEE.
Dr. Martine Ceberio University of Texas at El Paso [biography]

Dr. Ceberio is a professor of Computer Science at the University of Texas at El Paso, currently serving as Associate Dean of Engineering for People, Culture, and Environment. She joined UTEP in 2003 after obtaining her PhD in Computer Science from the University of Nantes, France (2003). Her research revolves around reliable decision-making under uncertainty, optimization, constraint solving, solving dynamical systems, and studying neural networks with uncertainty. Her passion lies in developing reliable tools to generate reliable information about otherwise uncertain situations. She is passionate about teaching and mentoring students. She is a fierce advocate of her students’ success and seizes every opportunity to provide advice and mentoring. At the University of Texas at El Paso, she is the co-lead of the Student Experience Project, which currently provides, within a community of practice, bringing together about 50 faculty and 15 graduates teaching assistants, and has so far reached over 4,500 students. As Associate Dean of Engineering, she leads a mentoring program for new faculty, which includes sessions on teaching. Outside UTEP, she mentors teachers of the local community and gets to share her passion beyond through contributed and invited talks about her teaching approach.
Dr. Alexandra Coso Strong http://orcid.org/0000-0003-4988-361X Cornell University [biography]

As an associate professor in the School of Chemical and Biomolecular Engineering and the Systems Engineering Program at Cornell University, Dr. Alexandra Coso Strong works and teaches at the intersection of engineering education, faculty development, and complex systems design. She joined Cornell University after co-founding the School of Universal Computing, Construction and Engineering Education at Florida International University (FIU). As an assistant professor at FIU, she co-developed two degree programs, a Ph.D. in Engineering and Computing Education and a B.S. in Interdisciplinary Engineering. Prior to working at FIU, Alexandra served as an Assistant Professor of Systems Design and Engineering at Olin College. Alexandra completed her graduate degrees in Aerospace Engineering from Georgia Tech (PhD) and Systems Engineering from the University of Virginia (UVa).

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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