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Artificial Intelligence: Resources and Information for Clinical Social Workers

Marla Galvan, LICSW, LCSW-C, Senior Practice Associate, Clinical Social Work

August 2026

Artificial intelligence (AI) is the ability of computer systems to perform tasks that typically require human intelligence (Reamer, 2025). It encompasses various complex models that together make up a rapidly evolving technology capable of understanding language, learning from data, and making predictions or recommendations (Borah et.al, 2026). Machine learning, generative AI, natural language processing, and large language models are just some of the AI technologies relevant to the mental health field.

Many clinical social workers (CSWs) recognize the possible applications for these diverse AI technologies and their potential for improving efficiency in practice. Yet there is little uniform guidance for the field on best practice to ensure ethical use (Borah et.al, 2026). As such, CSWs should take it upon themselves to obtain ample training and education on the positive use of this emerging area of practice and familiarize themselves with the ethical risks and limitations.

This Tips & Tools for Social Workers provides information on three central themes to support CSWs with continued learning: How AI is being used by CSWs, ethical and legal considerations, and AI limitations.


How AI is Being Used by CSWs

AI use is becoming a common feature in clinical social work practice. Clinicians are using nonpublic HIPAA-compliant consumer software products -often powered by generative AI or ambient listening technologies- to assist with documentation and other administrative tasks. Predictive tools, powered by machine learning and natural language processing, are being used in treatment planning to detect patterns and generate recommendations for client goals. Chatbots and other generative AI tools, powered by large language models, are additionally being used in clinical education and training to support practicum preparation and develop interactive lessons.

The following resources provide information about positive use cases of AI in clinical social work and how it is being used to streamline administrative tasks, analyze mental health data, and strengthen provider training.


Ethical and Legal Considerations

AI use in clinical social work comes with ethical and legal risks. Privacy, confidentiality, HIPAA compliance, and securing protected health information (PHI) and other client data are topics that consistently appear in the discussion related to the use of these tools, along with AI transparency and the need to provide clients with informed consent.

The resources in this section cover key information related to ethical and legal considerations for CSWs using AI tools in practice and information about emerging state laws governing use.


Limitations

AI tools built for use in mental health care have limitations. AI-generated notes or documentation are not always accurate. AI models lack the capacity to assess the environmental and cultural contexts necessary for holistic clinical decision making. And bias in AI system outputs may inadvertently reinforce discrimination related to race and ethnicity, and other forms of gender, sexual orientation, or socioeconomic prejudice.

The resources in this section focus on the limitations of AI in clinical social work, the importance of understanding and addressing bias in AI systems, and the fact that AI systems are not licensed to make therapeutic decisions and cannot replace the individual clinical voice.


Continued Learning

While state-by-state regulations for the use of AI in clinical social work practice and mental health care are beginning to emerge, the literature indicates that the clinical social work field is seeking more uniform guidance on the appropriate use of AI models in practice. In the absence of a national, cohesive regulatory framework, CSWs must take responsibility for their own AI literacy, skill development, and knowing acceptable use to help inform their work.

The resources below offer opportunities for additional education and training.


Conclusion

The use of AI in clinical social work practice and mental health care is complex and rapidly evolving. As such, NASW will continue to monitor trends in AI use in the field and provide updated resources and information to address ethical and legal concerns as well as new uniform guidance as it becomes available.


Resources

Borah, E., Meyerhoff, J., Al-Turk, A., Gower, K., Mastyukova, A. (2026). Use of Artificial Intelligence in Social Work Practice: Findings and Recommendations from a National Survey. The University of Texas at Austin Moritz Center for Societal Impact. https://moritzcenter.utexas.edu/focus-areas/health-technology/report-use-of-artificial-intelligence-in-social-work-practice/

Coyle, S (2026). AI and Clinical Social Work. National Association of Social Workers. AI and Clinical Social Work(https://www.socialworkers.org/News/Social-Work-Advocates/2026-Summer-Issue/AI-and-Clinical-Social-Work?_zs=dTO5p1&_zl=JoJnA)

Goldkind, L. (2025). AI and Social Work: Balancing Humanity and Technology. National Association of Social Workers, Social Work Talks Podcast. https://www.youtube.com/watch?v=Idc8mIFjZ50.

Reamer, F.G. (2025). Documentation Strategies: AI in Social Work. Social Work Today 25 (4),18. https://www.socialworktoday.com/archive/Fall25p18.shtml

The Meadows Institute (2026). Artificial Intelligence & Mental Health. https://mmhpi.org/topics/educational-resources/artificial-intelligence-mental-health/.