AI Work Group Recommendations

The American Counseling Association has convened a panel of counseling experts representing academia, private practice and students to comprise its AI Work Group. The work group used research-based and contextual evidence; the ACA Code of Ethics; and clinical knowledge and skill to develop the following recommendations. The goal is to both prioritize client well-being, preferences, and values in the advent and application of AI, while informing counselors, counselor-educators and clients about the use of AI today. The recommendations also highlight the additional research needed to inform counseling practice as AI becomes a more widely available and accepted part of mental health care.

For counseling faculty aiming to thoughtfully and effectively incorporate artificial intelligence (AI) and large language models (LLMs) into their educational practices, adopting best practices that emphasize ethical use, inclusivity, and the enhancement of learning objectives is crucial. Below are guidelines in designated areas of ethics, DEIA, teaching, essential tasks and specialty areas, to support faculty in navigating these integrations responsibly.


1. Consulting University-Specific Guidelines on AI Use
It is imperative for faculty to consult their respective institution's guidelines regarding the acceptable use of Artificial Intelligence (AI) technologies by students within academic settings. Given the rapid evolution of AI and its applications in education, university policies are continuously being updated to address ethical, legal, and academic integrity concerns. Faculty members should ensure that their course policies on AI use are in alignment with their institution's latest guidelines to foster a responsible and equitable learning environment. (Chan, 2023).

2. Ethical and Inclusive Teaching Practices
Incorporate regular discussions on the ethical implications of AI use, informed by the American Counseling Association (ACA) Ethics Code. Emphasize the significance of diversity, equity, and inclusion (DEI) in these conversations, encouraging a critical examination of how AI tools align with or challenge ethical standards in counseling (American Counseling Association, 2014; Woodcock et al., 2023).

3. Accuracy and Bias Evaluation in AI Content
Assess the accuracy of AI-generated content and scrutinize it for biases. This critical evaluation should extend to students, promoting their ability to identify culturally competent and inclusive content, thus preparing them to navigate the diverse landscape of counseling practices (Noble, 2018; Brault & Saxena, 2021)

Diversity, Equity, Inclusion, & Advocacy

4. Keeping Up-to-Date with AI Developments through a DEI Lens
Stay informed about the latest AI and LLM advancements, particularly those affecting or improving DEI in counseling practices. This knowledge is vital for integrating the most relevant and responsible technology into your curriculum (D'Ignazio & Klein, 2020; Cachat-Rosset & Klarsfeld, 2023).

5. Enhancing Resource Accessibility with a Diversity Focus
Leverage AI tools to compile and recommend educational resources that present diverse perspectives. This aids in supporting an inclusive curriculum and exposing students to a broad spectrum of counseling theories and practices (APA Presidential Task Force on Educational Disparities, 2012).


6. Preparing Students for Ethical Technological Integration
Educate students on the ethical integration of AI and LLMs into counseling practice, emphasizing service to clients from diverse backgrounds. Highlight the potential of technology to support ethical, inclusive, and culturally sensitive counseling practices (Furlonger & Gencic, 2019; Barnett & Kolmes, 2016).

7. Cultivating Critical Evaluation Skills for AI Interpretation
Teach students to critically assess AI-generated advice and content, highlighting the importance of recognizing and challenging biases. This practice fosters the development of culturally competent counseling professionals (Costa & Torres, 2019; van Gelder et al., 2004).

8. Development of Critical and Cultural Competencies
Utilize AI to complement traditional teaching methods, focusing on nurturing students' critical thinking, clinical skills, and cultural sensitivities. This approach ensures students are well-equipped to meet the diverse needs of future clients (Sue et al., 1992; Woods-Jaeger et al., 2024)..

9. Diverse and Inclusive Teaching Methods
Employ AI to create simulations or role-play exercises that reflect a broad spectrum of cultural contexts and client scenarios. This strategy enhances students' learning experiences and prepares them for real-world counseling diversity (Gonzalez, 2020; Dougherty et al., 2020)

10. Critical Virtual Counseling Ethics
In discussions on virtual counseling ethics, stress the importance of equitable access and respecting diverse identities. Explore how AI tools can either support or undermine ethical practices in digital counseling environments (Kaimara,et al., 2021).

11. AI for Diverse Case Study Creation
Use AI to develop case studies that cover a wide range of human experiences, ensuring these materials are inclusive of different cultures, identities, and life situations. This approach promotes a comprehensive and diverse learning environment (Nah, et al., 2023).

Essential Work Tasks

12. Using AI for Essential Work Tasks
Faculty members need to approach AI integration with caution, particularly when it involves critical tasks such as note-taking during meetings, generating student feedback, and creating comprehensive curriculum and lectures. While AI offers significant benefits in terms of efficiency, it is essential to evaluate its effects on academic integrity, the quality of personalized interaction, and the risk of creating a depersonalized educational experience. Faculty should strategically use AI tools to support and enhance the educational process without compromising the essential personal touch that is especially vital in the field of counseling education.

Specialty Areas

13. AI Integration in Clinical Supervision
Counselor educators, as supervisors, must ensure AI use respects privacy and complies with HIPAA, emphasizing human judgment over AI, especially in ethical decision-making and areas lacking AI's emotional understanding. It is vital to address AI biases for fair application and to balance AI's advantages with nurturing students' critical skills. Supervisors should stay informed about AI's legal and regulatory issues, ensuring AI augments rather than replaces the essential values of counseling education and professional practice.

14. Understanding the Implications of A.I. with Research
Faculty members should consider the role of artificial intelligence (AI) in research with caution and awareness. AI tools, including article finders and narrative generators, can enhance study development, but it is crucial to recognize that students might be using AI without faculty knowledge, potentially affecting research integrity. Faculty can ensure responsible use and guide students in leveraging AI's potential responsibly and effectively in their research endeavors.

Warnings and Concerns

While the aforementioned practices offer a framework for ethically and effectively incorporating AI and LLMs into counseling education, there are essential warnings and concerns to bear in mind:

Ethical Vigilance
Faculty must maintain awareness of the ethical implications of AI and LLM use, especially concerning privacy, confidentiality, and bias reinforcement. It is imperative to align these technologies with professional ethical standards and adjust practices as needed (Buchanan & Zimmer, 2018; Carlisle, et al., 2022).

Bias and Cultural Competence
AI and LLM technologies may inadvertently perpetuate biases. Faculty should actively seek tools developed or trained to minimize bias, emphasizing the cultivation of cultural competence in AI use (Benjamin, 2019; Chun et al., 2020).

Technological Reliance
Avoid over-reliance on AI and LLMs for content and teaching methods. Balancing technology with traditional teaching ensures students develop essential counseling skills based on human judgment and empathy (Turkle, 2017; Ng et al., 2023).

Accuracy and Misinterpretation
Be cautious of AI-generated content's accuracy and contextual appropriateness to prevent misinterpretations. Training is needed to critically assess AI content, particularly in complex counseling scenarios (Nah, et. al., 2023).

Technology Evolution
The rapid development of AI and LLMs necessitates regular revisiting and revising of best practices. Continuous learning about technological advancements is crucial to adapt teaching practices and curriculum effectively (Ford, 2018).

Access and Equity
Integrating AI and LLMs must consider access and equity issues. Advanced technologies should not exacerbate disparities among students and clients with different digital resource access levels (DOE Office of Educational Technology (n.d.)


American Counseling Association. (2014). ACA Code of Ethics.

APA Presidential Task Force on Educational Disparities. (2012). Ethnic and racial disparities in education: Psychology’s contributions to understanding and reducing disparities.

Barnett, J. E., & Kolmes, K. (2016). The practice of tele-mental health: Ethical, legal, and clinical issues for practitioners. Practice Innovations, 1(1), 53–66.

Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity.

Brault, N., & Saxena, M. (2021). For a critical appraisal of artificial intelligence in healthcare: The problem of bias in mHealth. Journal of Evaluation in Clinical Practice, 27(3), 513–519.

Buchanan, R., & Zimmer, M. (2018). Internet research ethics. In P. H. (Ed.), Encyclopedia of applied ethics.

Cachat-Rosset, G., & Klarsfeld, A. (2023). Diversity, Equity, and Inclusion in Artificial Intelligence: An Evaluation of Guidelines. Applied Artificial Intelligence, 37(1).

Carlisle, K. L., Levitt, D. H., & Neukrug, E. S. (2022). Mental Health Counselors’ Perceptions of Ethical Behaviors. Counseling & Values, 67(1), 88–115.

Chan, C.K.Y. A comprehensive AI policy education framework for university teaching and learning. Int J Educ Technol High Educ 20, 38 (2023).

Chun, J., Connor, A., Alsaman, M., Urkmez, B., & Kosciulek, J. F. (2020). Capitalizing on Diversity in Counselor Education: An Application of the Interaction for Learning Framework. Journal of Multicultural Counseling & Development, 48(3), 161–175.

Costa, L. M., & Torres, C. A. (2019). Critical literacy: Theories and practices in a critical pedagogy. Routledge.

D'Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

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Dougherty, A. E., Haddock, L., & Patton, J. (2020). Exploring the use of mindful andragogy to create inclusive classrooms. Journal of Creativity in Mental Health, 15(1), 43–54.

Ford, M. (2018). Architects of intelligence: The truth about AI from the people building it. Packt Publishing.

Furlonger, B. E., & Gencic, E. M. (2019). Evidence-based practice in school mental health. Oxford University Press.

Gonzalez, L. (2020). Inclusive counseling: Working with LGBTQI+ clients. Cengage Learning.

Kaimara, P., Oikonomou, A., & Deliyannis, I. (2021). Could virtual reality applications pose real risks to children and adolescents? A systematic review of ethical issues and concerns. Virtual Reality, 25, 1-22.

Nah, F-H, Zheng, R., Cai, J., Siau, K., & Langtao C. (2023): Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration, Journal of Information Technology Case and Application Research, DOI:

Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161.

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Sue, D. W., Arredondo, P., & McDavis, R. J. (1992). Multicultural counseling competencies and standards: A call to the profession. Journal of Counseling & Development, 70(4), 477-486.

Turkle, S. (2017). Alone together: Why we expect more from technology and less from each other. Basic Books.

van Gelder, T., Bissett, M., & Cumming, G. (2004). Cultivating expertise in informal reasoning. Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, 58(2), 142–152.

Woodcock, S., Gibbs, K., Hitches, E., & Regan, C. (2023). Investigating Teachers’ Beliefs in Inclusive Education and Their Levels of Teacher Self-Efficacy: Are Teachers Constrained in Their Capacity to Implement Inclusive Teaching Practices? Education Sciences, 13(3), 280.

Woods-Jaeger, B., Cho, B., & Briggs, E. C. (2024). Training psychologists to address social determinants of mental health. Training and Education in Professional Psychology, 18(1), 31–41.

AI Work Group Members

S. Kent Butler, PhD
University of Central Florida
Russell Fulmer, PhD
Husson University
Morgan Stohlman
Kent State University
Fallon Calandriello, PhD
Northwestern University
Marcelle Giovannetti, EdD
Messiah University- Mechanicsburg, PA
Olivia Uwamahoro Williams, PhD
College of William and Mary
Wendell Callahan, PhD
University of San Diego
Marty Jencius, PhD
Kent State University
Yusen Zhai, PhD
UAB School of Education
Lauren Epshteyn
Northwestern University
Sidney Shaw, EdD
Walden University
Chip Flater
Dania Fakhro, PhD
University of North Carolina, Charlotte