Summer 2025 Issue

AI-Powered Social Work Training
By Heather Rose Artushin, MSW, LISW-CP
Social Work Today
Vol. 25 No. 3 P. 10

Examining the Ethical Use of AI in Social Work Education

AI technology is advancing at lightning speed, integrating into many aspects of daily living, including social work education and practice. Concerns about how professors and students are using AI in the classroom are at the forefront of the conversation when it comes to social work training today.

The Council on Social Work Education, the accrediting authority for social work programs in the United States, cites a commitment to increasing knowledge of AI and its implications for social work education as its first 2026 strategic plan goal in order to anticipate both “the implications and opportunities of AI.”1 As AI is infiltrating our world, many are left wondering what boundaries should be placed around this burgeoning technology.

The use of AI in social work education reflects a broader trend to utilize the technology in social work practice across micro, mezzo, and macro levels. In the field, social workers are trying out several applications of AI to enhance clinical decision-making, streamline workflow, and increase access to care. Predictive analytics help social workers to more easily identify at-risk populations, while virtual assistants and chatbots, like Woebot and Wysa, increase access to mental health care using cognitive behavioral therapy techniques, mood tracking, and more. Natural language processing identifies patterns of emotion across documents, like case notes, to identify early signs of distress or escalating risk. As social workers are increasingly using AI in practice, it is important to consider the role of AI in social work education.

The Future of Social Work Education
Interfacing with AI technology in social work training may be a necessary step to prepare social work students for a career in an increasingly technology-driven world.2 Social work educators are already beginning to implement AI technology in the classroom, using ChatGPT as a digital research assistant to reduce course planning time. Clinical social work students may even interact with ChatGPT as if it were a client, using the technology as an opportunity to begin exercising interpersonal practice skills.3

David Hodgson, PhD, an associate professor in the social work program at Curtin University in Perth, Australia, published a paper on the implications of AI in social work education in the British Journal of Social Work in 2022,4 and has continued to explore the intersection of AI and social work through writing, research, and speaking opportunities. “I am approaching my engagement with AI from a critical perspective, trying to understand the broad social, political, economic, and ethical implications of AI transformations on social work, research, education, and society more broadly,” he says.

The ways that AI will ultimately change the shape of social work education are unknown, though experts like Hodgson have ideas about how this technology might become an integral part of academia in the near future. “AI may become part of the move towards simulated learning, as an adjunct pedagogy to field placement,” Hodgson suggests. “AI might be deployed in a supervising or tutoring role, to give feedback and direction on student work and performance. It is also being touted as an efficiency and productivity tool, so this might involve higher output expectations for academics, who will be expected to use AI to hasten the speed and volume of their academic work. The result could be less time spent in direct contact with students, using AI to perform key functions of academic work and administration. Overall, human critical thinking and the ability to undertake complex decision-making are going to be indispensable skills to teach and acquire for practice.”

Social work educators can lead the way in fostering critical thinking skills in their students, encouraging them to examine the contributions of AI through the lens of social work competencies, ethics, and person-first logic. “There’s the risk that overreliance on AI tools could diminish the value of professional judgment, empathy, and human connection, core strengths of social work practice. To counter this, education must emphasize the role of critical thinking, ethical decision-making, and the necessity of human oversight in technology-assisted environments,” Caleb Kadiri urges, whose background sits at the intersection of health care, public health, and AI. Kadiri collaborated with the School of Social Work at Morgan State University and the Data Science Research Lab in the department of computer science to organize the “Data Science for Social Work” conference, which explored critical issues like algorithmic bias and ethical AI integration. “Students should also be trained to recognize the limitations of AI and to challenge its outputs when necessary, especially when they conflict with culturally competent or trauma-informed approaches.”

Above all, the ability for social work students to think critically about AI will become increasingly important to avoid complacent reliance on this shifting technology that is far from infallible. “Crucially, all the literature I have read on AI and higher education emphasizes the centrality of critical thinking skills,” Hodgson explains. “Students and practitioners will need very high levels of advanced critical thinking to evaluate and critique AI systems, their inputs, and outputs, and they will need a solid disciplinary and interdisciplinary knowledge base so they can verify and evaluate the outputs of AI systems. This goes well beyond doing fact checking, but being able to spot bias, epistemic gaps, fabrications, cultural misinterpretations, misleading outputs, and other legal, ethical, and social justice implications from wide-scale deployment of AI systems into health and social welfare organizations, policies, laws, and other governing apparatus.”

Collaboration with other university departments to implement a more interdisciplinary approach to AI education may help to adequately address these issues for the benefit of tomorrow’s social workers. “Programs may partner with departments in computer science, public health, or information systems to coteach courses, engage in cross-disciplinary projects, or explore how AI intersects with social determinants of health,” Kadiri explains. “This integration will ensure that future practitioners are fluent not only in social work values but also in the language and logic of technology. Ultimately, the future of social work education will be defined by its ability to bridge compassion with computation and ensure that while AI tools are used to enhance outcomes, they never replace the core human elements of empathy, dignity, and justice.”

Ethical Considerations
While AI may become an inevitable staple in social work education and practice as we step forward into the future, there are significant ethical considerations that must be addressed in order to embrace technology without compromising social work’s values. Key ethical issues surrounding the use of AI in social work education include plagiarism, algorithmic bias and unfairness, and misinformation. While students and teachers might use ChatGPT to conduct preliminary research or produce written content, it is important to properly cite sources to avoid plagiarism, dishonesty, or misrepresentation. Though using content from Chat-GPT is not necessarily considered plagiarism in itself, ChatGPT often incorporates content from other sources that must be cited.5 “Because they are time poor, there is temptation for students to overuse AI for their studies, beyond what is acceptable,” Hodgson says. “Furthermore, what is an acceptable use of AI for learning is contested, with little clear institutional guidance.”

In order to prevent an increase in plagiarism, instructors may need to restructure course expectations to include methods of testing knowledge and critical thinking skills beyond writing papers. “Detecting AI-generated work remains a challenge, as current plagiarism detection software may not effectively identify content produced by advanced language models. Moreover, AI-generated writing can closely mimic a student’s voice, making it harder for instructors to distinguish between original and machine-assisted work,” Kadiri explains. “To address this, professors must rethink traditional assessment strategies. Instead of relying solely on written assignments, they may need to incorporate oral defenses, in-class discussions, or process-based assessments (eg, drafts, peer reviews, project logs) to better gauge authentic learning. At the same time, it’s important to guide students on how to use AI tools ethically as aids for brainstorming, research, and editing rather than as shortcuts. Embedding discussions about the responsible use of AI into the curriculum can foster transparency, accountability, and critical digital literacy.”

Social work education is fundamentally relational, raising questions about how an increased reliance on technology might compromise this interpersonal aspect of social work training. “While curriculum concerns ‘what’ is taught, pedagogy concerns ‘how’ it is taught,” Hodgson explains. “Social work pedagogy works best when there is a good instructor-student relationship, because it should align with and reflect the contexts and realities of practice. That is, the pedagogy—in a relational form—should foster and model excellent interpersonal communication skills, respect, dignity, and inclusion, compassion and self-compassion, reflection, dialogue, critical thinking, and civil discussion and debate over contested and uncertain matters. These and other aspects are the relational aspect of teaching that will be lost or threatened if teaching becomes standardized, packaged, and digitized into didactic ‘blocks’ of content delivered by an AI platform. Face-to-face teaching is important to a social work relational pedagogy that teaches and models good social work practice.”

Algorithmic bias is another serious concern that directly opposes the social work profession’s commitment to diversity, equity, and inclusion. It has been discovered that some AI algorithms tend to replicate, and even amplify, human biases against marginalized groups of people.5 “Given the profession’s mandate to advocate for marginalized populations, students must learn how to question whether an AI system is reinforcing inequities or misrepresenting client needs due to biased training data,” Kadiri says. “The risk of automating inequity through poorly designed or inadequately monitored AI tools makes ethical and cultural competence as vital as technological skills.”

Schools of social work might consider forming a digital ethics steering committee that examines the implications of technological advances for the field of social work and how they might be ethically applied to social work education. Education and critical thinking are key when it comes to approaching how AI will be used by social workers in training and in the field. Requiring courses on the topic of AI ethics for incoming social work students may prepare the next generation of social workers to practice alongside technology while leading with their values and the profession’s ethical principles in mind.

In 2013, the Association of Social Work Boards’ board of directors appointed an international task force to develop model regulatory standards for technology and social work practice. The extensive ethics guidelines related to technology use in the social work profession can prove to be a valuable resource as social workers today approach integrating AI into social work education and practice.5

Holding Fast to Social Work Values
Social work is a profession guided by values and ethics that prioritize humanity, and now more than ever it is important for social workers to lean into those values. The NASW prompts social workers to examine ways to harness the benefits of AI while mitigating its risks to ensure the use of AI in social work education and practice aligns with the values and standards of the profession’s code of ethics.6

“I am deeply concerned about the impact that AI will have on social work education and practice, and on society more broadly. I hope social workers take away the centrality of adopting a critical perspective on AI and resist the temptation to take on the AI-hype being espoused by the big tech companies, that AI will usher in a golden era—I am skeptical of that claim,” Hodgson says. “I also hope social workers resist the ‘efficiency’ discourse, which plays into the structures and cultures of extractive capitalism that constantly pushes people to do more with less, and that they understand that large language models (LLMs) can produce a lot of nonsense, which can mean that knowledge, science, and learning may eventually become a series of recursive, recycled, or distorted outputs from LLMs, and not genuine, rigorous evidence or theory for practice.”

Keeping in mind the potential pitfalls of AI, social workers can play an integral role in holding one another and society as a whole accountable for the ways that this technology is changing the human experience. “It’s essential that social workers, educators, and technologists understand that adopting AI is not a neutral act; it must be guided by the core values of the profession—human dignity, equity, social justice, and client-centered care,” Kadri says. “I hope readers see that the future of social work does not lie in choosing between tradition and technology, but in combining the two. The goal is not to replace human judgment or empathy, but to empower practitioners with tools that enhance their ability to serve diverse communities more effectively and equitably. By engaging critically with AI, social workers can become not only users but also advocates and cocreators of technologies that uphold the values at the heart of the profession.”

While the future possibilities of AI might feel exciting, overwhelming, and even a little frightening, it is important to remember all the changes that social workers have adapted to through the years. From the birth of the social work profession in the early 1900s, social workers have witnessed many shifts in technology, culture, politics, and research advancements. Through it all, social workers have continued to be person-focused, celebrating differences, respecting each person’s worth and dignity, and fighting for social justice and equity every step along the way. This generation of social workers will witness ever more changes, many the result of AI advances, and will hold fast to social work values and principles that will always put human relationships at the heart of this important work.

“The goal is not to turn social workers into technologists, but to develop ethically informed, tech-savvy practitioners who can confidently lead in AI-integrated environments. By aligning educational goals with emerging technological realities, social work programs can ensure that future professionals are equipped to use AI tools as instruments of empowerment and tools that enhance, rather than compromise, the values of social justice, equity, and human dignity at the heart of the profession,” Kadiri says. “Ultimately, reimagining educational goals through the lens of emerging technology will ensure that future social workers remain not just users of AI but informed stewards of its ethical and equitable application.”

— Heather Rose Artushin, MSW, LISW-CP, is a writer with over a decade of published experience and a passion for social justice.

References
1. 2026 strategic plan. CSWE website. https://www.cswe.org/about-cswe/2026-strategic-plan/. Published 2024.

2. Nuwasiima M, Ahonon MP, Kadiri C. The role of artificial intelligence (AI) and machine learning in social work practice. World Journal of Advanced Research and Reviews. 2024;24(1):080-097. https://doi.org/10.30574/wjarr.2024.24.1.2998

3. Faught M. Texas educators explore generative AI adoption. The Et Cetera website. https://eastfieldnews.com/27763/news/texas-educators-explore-generative-ai-adoption/. Published May 2, 2023.

4. Hodgson D, Goldingay S, Boddy J, Nipperess S, Watts L. Problematising artificial intelligence in social work education: challenges, issues and possibilities. Br J Soc Work. 2021;52(4):1878-1895. https://doi.org/10.1093/bjsw/bcab168

5. Reamer FG. Artificial intelligence in social work: emerging ethical issues. International Journal of Social Work Values and Ethics. 2023;20(2):52-71. https://doi.org/10.55521/10-020-205

6. Dale M. ChatGPT and social work: be excited, curious, and skeptical. National Association of Social Workers website. https://www.socialworkers.org/News/Social-Work-Advocates/June-July-2023-Issue/ChatGPT-and-Social-Work-Be-Excited-Curious-and-Skeptical. Published 2023.

Resources
Ahn E, Choi M, Fowler P, Song I. Artificial intelligence (AI) literacy for social work: implications for core competencies. J Soc Social Work Res. 2025;16(1). https://doi.org/10.1086/735187

AI and social work. National Association of Social Workers website. https://www.socialworkers.org/About/Ethics/AI-and-Social-Work