Kahyen Shin, M.Ed, NCC, is a Ph.D. student in the Counseling & Human Services unit within the School of Education at Syracuse University. Her research is centered on cross-cultural individuals, including refugees, third-culture kids (TCKs), and immigrants. Believing strongly in research as a powerful tool for advocacy, she is dedicated to anti-oppressive practices in her field, aiming to amplify the voices and experiences of marginalized groups.
Melissa Luke, PhD, LMHC, NCC, ACS, is a Dean’s Professor in the unit of Counseling & Human Services within the School of Education at Syracuse University. She is a National Certified Counselor (NCC), an Approved Clinical Supervisor (ACS), and both a Licensed Mental Health Counselor (LMHC) and certified School Counselor in the State of New York. Dr. Luke is an international leader in counselor education with over 200 publications and has delivered presentations and workshops in seven countries to date. Dr. Luke is a Fellow in the American Counseling Association and Association for Specialists in Group Work. In addition, she was the 2024 recipient of ACA’s Extended Research Award. Dr. Luke’s scholarship uses both critical qualitative and quantitative methods to explore anti-oppressive counselor preparation and practice to more effectively respond to the needs of underserved persons, as well as examine supervision and group counseling processes. Extending her group work scholarship, Dr. Luke has provided extensive training for faculty, staff, students, administrators, local communities, and international partners on diversity, equity, inclusion, accessibility, and anti-oppression (DEIAO). These multipronged training sessions aided educators and community partners in building anti-oppressive and anti-racist group skills for responsive practices in educational and community settings.
Document Type
Article
Keywords
Artificial Intelligence, ChatGPT coding, Interpretive Phenomenology, Anti-Oppressive Research
Subject Area
Counselor Education
Abstract
This study explores the concordance and discordance between human researchers and ChatGPT in conducting Interpretative Phenomenological Analysis (IPA) using an interview transcript with a refugee resettling in the United States. Findings revealed both alignment and discrepancies between ChatGPT and human researchers; while ChatGPT effectively identified themes and coding structures with interpretative depth and reflective engagement, it occasionally struggled to capture implicit meanings, particularly in narrative continuity and cultural-contextual analysis. These challenges were evident in its misinterpretation of past and present events, difficulty in recognizing nuanced associations, and tendency to adopt a deficit-based lens in certain analyses. Findings are discussed considering the emergent AI research literature, with attention to culturally responsive and anti-oppressive research practices with historically marginalized populations.
DOI
http://dx.doi.org/10.70013/t32jkpeq
Recommended Citation
Shin, K. & Luke, M. M. (2025). A descriptive comparison of results from human and AI-driven Interpretive Phenomenological Analysis. Journal of Counselor Preparation and Supervision, 19(3), 1-15.
