An Interpretative Phenomenological Analysis of Counselor Educators’ Experiences of Using Artificial Intelligence in Their Teaching

ABSTRACT Over the last few years, AI integration has rapidly increased in counseling and counselor education. Given its prevalence, counselor educators have a responsibility to consider AI in their teaching to prepare students to intentionally and critically use AI in their careers. Yet, limited research exists to understand counselor educators’ decisions and meaning making regarding AI in teaching. In this Interpretative Phenomenological Analysis study, we conducted individual interviews and a focus group interview and collected documents (e.g., syllabus statements, assignments) to examine counselor educators’ experiences using AI in their teaching and promote intentional and reflective use of AI. The resulting themes of our data analysis were (a) concerns and risks, (b) navigating complex emotions, (c) responsibility to students and the profession, (d) influence of systems, and (e) application of AI in teaching. Participants shared engaging in a trial‐and‐error process of experimenting with AI in their teaching considering the lack of policies and resources from institutions and counseling organizations which could cause feelings of shame and fear resulting from the lack of open dialogue about AI in counselor education. Implications for counselor educators and institutions include creating continuously updated AI policies and resources and offering spaces where counselor educators can discuss challenges, successes, and feelings and share resources regarding AI in teaching.

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Publication Details

Journal
Counselor Education and Supervision
Published
2026-03-31
DOI
https://doi.org/10.1002/ceas.70026
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

An Interpretative Phenomenological Analysis of Counselor Educators’ Experiences of Using Artificial Intelligence in Their Teaching

Phillip L. Waalkes, Jaimie Stickl Haugen, Daniel Andrew DeCino, Daisy Zhaoxuan Zhou et al.
Counselor Education and Supervision
Intelligent Tutoring Systems and Adaptive Learning
article

An Interpretative Phenomenological Analysis of Counselor Educators’ Experiences of Using Artificial Intelligence in Their Teaching

Phillip L. Waalkes, Jaimie Stickl Haugen, Daniel Andrew DeCino, Daisy Zhaoxuan Zhou, Daniel P. Hall, Jacqueline M. Swank Fastenow
article en

Abstract

ABSTRACT Over the last few years, AI integration has rapidly increased in counseling and counselor education. Given its prevalence, counselor educators have a responsibility to consider AI in their teaching to prepare students to intentionally and critically use AI in their careers. Yet, limited research exists to understand counselor educators’ decisions and meaning making regarding AI in teaching. In this Interpretative Phenomenological Analysis study, we conducted individual interviews and a focus group interview and collected documents (e.g., syllabus statements, assignments) to examine counselor educators’ experiences using AI in their teaching and promote intentional and reflective use of AI. The resulting themes of our data analysis were (a) concerns and risks, (b) navigating complex emotions, (c) responsibility to students and the profession, (d) influence of systems, and (e) application of AI in teaching. Participants shared engaging in a trial‐and‐error process of experimenting with AI in their teaching considering the lack of policies and resources from institutions and counseling organizations which could cause feelings of shame and fear resulting from the lack of open dialogue about AI in counselor education. Implications for counselor educators and institutions include creating continuously updated AI policies and resources and offering spaces where counselor educators can discuss challenges, successes, and feelings and share resources regarding AI in teaching.

Counselor Education and Supervision
Texas State University (US), William & Mary (US), University of South Dakota (US), University of Missouri–St. Louis (US), University of Lynchburg (US), Virginia University of Lynchburg (US), University of Missouri (US)
Quality Education
Openalex Percentile: Top 34%
Intelligent Tutoring Systems and Adaptive Learning
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