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📚 Generative AI for social work students- Part I


💡 Newskategorie: AI Nachrichten
🔗 Quelle: towardsdatascience.com

Generative AI for social work students- Part I

A paradigm shift

ASCII art of a puppy produced by the author using ChatGPT.

Artificial intelligence (AI) has advanced incredibly fast in recent years, giving rise to powerful tools known as generative AI and large language models. Generative AI is artificial intelligence that can create new content, such as text, images, or music, based on patterns learned from existing data. Large language models, like OpenAI’s GPT series, are a subset of generative AI that have been specifically trained on vast amounts of text to understand and generate human-like language. These models have become increasingly sophisticated, allowing them to produce coherent, contextually relevant, and even creative outputs.

Over a year ago, I began using GPT and was amazed by its ability to generate text that is indistinguishable from a human writer. This ability also brought up concerns regarding academic dishonesty. Consequently, I authored an article on Medium anticipating the emergence of AI-assisted academic misconduct.

Since then, I have closely monitored developments in generative AI and explored how they can complement my interest in traditional AI systems. Recently, my colleagues and I evaluated ChatGPT’s capacity to pass a simulated Masters-level social work licensing exam — and it did. Our study’s pre-print is available here and will soon be published in Research on Social Work Practice. ChatGPT now accomplishes various complex tasks at lightning speeds I never imagined possible just a year ago.

Image by the author.

As social work students, you might wonder how these technological advancements can impact your educational experience. AI can revolutionize how you learn, research, and engage with the complex challenges of social work. Large language models can do much more than generate or re-write text. You can use these models to summarize lengthy policy documents, analyze quantitative and qualitative data, generate insights for program evaluation, automate routine functions in a community organization, create marketing materials, build web applications and websites — and many more.

I’m convinced that you can enhance your learning experience and open doors to new professional experiences by developing a solid foundation in the understanding of AI technology and the skills to use them effectively. At the same time, these models pose significant challenges and risks to your learning and career without the right mindset, training, and experience.

This article is the first part of a series that aims to provide MSW students a starting point for learning about and building competencies in AI technologies, emphasizing large language models. This first article provides a broad overview of different applications of generative AI in social work, along with an initial discussion of ethical issues that will continue in the subsequent articles. The forthcoming topics include:

  • Part II: Essential knowledge, abilities, and practices with AI
  • Part III: Strategies for enhancing your educational experience with AI
  • Part IV: The art and science of prompt engineering

Applications of Generative AI in Social Work

Generative AI tools are emerging as a transformative force in various fields, including business, medicine, and law. Although AI technologies are just starting to get attention in social work, now is the ideal time for students to think actively about leveraging this tool to enhance and streamline service delivery, empower vulnerable populations, and foster more equitable outcomes.

I’m providing a few examples outlining how we can use generative AI. These examples are not comprehensive or fully representative of what generative AI can do. Keep in mind that the landscape of AI-enabled technologies is vast and ever-evolving. According to Sundar Pichai, CEO of Google and Alphabet, AI’s power doubles every six months.

Mental Health Services

One promising application of large language models in the field of social work lies within mental health services. Virtual therapy assistants, powered by AI, can offer support and guidance to clients between therapy sessions or when a social worker is not immediately available. For example, virtual assistants could help clients practice coping strategies, provide psychoeducation, or offer brief stress-reduction interventions. These would be assistants that could support various tasks but would not replace social workers.

Large language models can assist social workers in better understanding their clients’ language patterns. By analyzing text-based communication or transcriptions of therapy sessions, AI can identify patterns, themes, and sentiments that may provide valuable insights into a client’s emotional state, challenges, and progress in therapy. For instance, AI could help detect signs of depression, anxiety, or other mental health issues by analyzing a client’s word choices, tone, or expression of emotions.

Another practical application of large language models is streamlining the documentation process. Social workers can benefit from tools that help manage extensive paperwork demands, such as writing case notes, treatment plans, and progress reports, which can be time-consuming and detract from the time spent with clients. By leveraging AI, social workers can reduce the time and effort spent on administrative tasks, allowing them to focus more on direct client care and improving the overall quality of mental health services.

Program Administration

Generative language models can play a significant role in improving program administration within social work organizations. Managing and coordinating social work programs involve a lot of writing-intensive tasks, such as drafting reports, creating and updating training materials, and communicating with stakeholders. AI-powered tools can help generate text using raw data and existing templates as a starting point for these tasks. By automating the initial drafting process, social workers can save time and focus on refining and tailoring content to ensure it accurately reflects the program’s impact and outcomes.

This type of support can lead to more efficient reporting and better communication of program results to stakeholders, funders, and policymakers. Large language models can also assist in developing and updating training materials for social work programs. By analyzing existing content and incorporating new research or best practices, AI can suggest revisions and additions to training materials that ensure they remain up-to-date and effective.

Policy Analysis

Generative AI can be a valuable asset for social workers engaged in policy analysis, a crucial aspect of the social work profession that involves reviewing, critiquing, and developing policies to address social issues. Policy analysis often requires social workers to navigate complex and lengthy policy documents, which can be time-consuming and challenging. Large language models can help social workers synthesize and evaluate policy documents more efficiently. By extracting critical information and summarizing the main points, AI-powered tools can enable social workers to quickly grasp the essentials of a policy without having to read through the entire document. This workflow can save time and allow social workers to focus on analyzing the policy’s implications, strengths, and weaknesses.

Another useful application of generative AI in policy analysis is its ability to identify gaps and opportunities in existing policies. By analyzing and comparing multiple policy documents, AI can recognize patterns, trends, and areas where policies may be lacking or inconsistent. AI can help social workers pinpoint areas that require further attention or new approaches to address unmet needs. In addition, large language models can assist social workers in generating policy recommendations based on the identified gaps and opportunities. By drawing on a vast knowledge base, AI can suggest evidence-based solutions and best practices for policy development.

Program Evaluation

Program evaluation often requires social workers to analyze large amounts of qualitative and quantitative data to determine a program’s success and identify improvement areas. Large language models can assist social workers in analyzing program data more efficiently and generating insights that may take time to be apparent. For example, AI-powered tools can help analyze qualitative data, such as interviews and focus groups, by identifying participants’ themes, patterns, and sentiments. Thus AI-powered tools can provide social workers with valuable information about the experiences and perspectives of clients and other stakeholders, which can inform program improvements.

Similarly, AI can analyze quantitative data, such as program outcome measures, by identifying trends, correlations, and anomalies. By automating the initial stages of data analysis, social workers can focus on interpreting the results and applying their expertise to make data-driven decisions and recommendations.

By leveraging the insights generated by AI-powered data analysis, social workers can assess the efficiency and effectiveness of their programs and identify areas for improvement. Large language models can also help social workers explore alternative strategies and interventions by suggesting evidence-based practices and generating potential solutions based on the analysis of similar programs or situations.

Moreover, generative AI can assist in communicating evaluation findings by generating clear, concise, and accessible reports that effectively convey the results and recommendations to various stakeholders. Such processes help ensure that program evaluation findings are understood and acted upon, ultimately leading to better outcomes for clients and communities.

Community Organizing

Generative AI can play a significant role in supporting community organizing efforts. A critical aspect of community organizing is effective communication and outreach to mobilize individuals, groups, and organizations around a shared goal or issue.

Large language models can help social workers create targeted, engaging, and culturally-sensitive messages that resonate with diverse audiences. By tailoring messages based on different community members’ preferences, language, and concerns, AI-powered tools can improve the effectiveness of outreach campaigns and help build stronger connections with community members. Additionally, AI can assist in identifying trends and emerging issues in online conversations and social media, allowing social workers to stay informed and responsive to the community’s needs and priorities.

Generative AI can also help streamline collaboration and resource management in community organizing efforts. By automating the creation and updating of shared documents, tracking progress on tasks, and coordinating schedules, AI-powered tools can save time and improve the overall efficiency of organizing initiatives. Furthermore, social workers and community organizers can use AI to analyze data on community resources and needs, which enables them to identify gaps and opportunities for collaboration. This support, in turn, allows social workers to make informed decisions about resource allocation and partnerships to maximize the impact of their efforts.

Generative AI can provide valuable assistance in community organizing by helping develop advocacy materials and policy proposals. This practical application involves AI-powered tools analyzing existing policies, research, and best practices to help social workers create persuasive, evidence-based materials that accurately convey the community’s needs and priorities. Social workers and community organizers can use these materials to engage policymakers, funders, and other stakeholders in their advocacy efforts to promote systemic change.

International Social Work

Generative AI can be a valuable tool in international social work, where professionals often face the challenges of language barriers and cultural differences. With their advanced natural language processing capabilities, large language models can help social workers communicate more effectively with diverse populations by providing real-time translations and generating culturally-sensitive messages.

Large language models can improve cross-cultural understanding and facilitate collaboration between social workers and the communities they serve. In international social work, staying informed about global trends, emerging issues, and best practices that can inform interventions and policy recommendations is crucial. Generative AI can help social workers access and analyze vast amounts of information from different sources, countries, and languages. By synthesizing this information, AI-powered tools can provide social workers valuable insights and evidence-based strategies to address complex social issues across different contexts.

Generative AI can help foster collaboration and knowledge exchange among social work professionals in different countries and settings. AI-powered tools can help social workers share their expertise, learn from one another, and develop innovative solutions that transcend national and cultural boundaries by facilitating communication, generating summaries of research and reports, and identifying common challenges and opportunities.

International social work often involves designing, implementing, and evaluating programs and interventions across various cultural, social, and political contexts. Generative AI can support these efforts by helping social workers analyze contextual factors, identify locally-relevant strategies, and evaluate the effectiveness of interventions in different settings. By providing data-driven insights and recommendations, AI can empower social workers to adapt and improve their programs to serve diverse communities better and address global social challenges.

Generative AI presents promising opportunities in social work; however, acknowledging this technology’s imperfections is crucial. By carefully navigating potential benefits and drawbacks, we can ensure responsible and ethical use in supporting vulnerable communities.

An imperfect image of a computer generated by GPT-4

Ethical Considerations

AI tools are not impartial and can perpetuate biases, exacerbating systemic discrimination. Consequently, vigilance is necessary when addressing ethical concerns and implications surrounding AI integration in our work. Ethical consideration is paramount in social work, as AI can influence decisions that impact people’s lives.

Social workers must evaluate the ethical consequences of implementing AI, ensuring alignment with the profession’s core values: social justice, respect for human dignity, and ethical responsibility. Moreover, social workers have a crucial role in advocating for protecting vulnerable populations affected by AI, such as individuals with disabilities, marginalized communities, and children and youth. As an introduction to a broader conversation, I emphasize two practical discussion points related to the ethics of AI.

Understanding and addressing model bias

Generative AI and large language models learn from lots of data, which can include biases. In social work, we need to anticipate and protect against these biases to avoid causing harm with their use. Social workers should carefully monitor biases in biases in AI-created content, carefully checking outputs that could support stereotypes, spread false information, or show other biases.

The intimate connection between AI ethics and promoting diversity, equity, and inclusion becomes apparent when examining their shared goal of tackling bias in AI systems. Minimizing the likelihood of biases emerging in AI model creation and implementation requires the participation of people with diverse backgrounds and perspectives. Encouraging better representation and participation of underrepresented and marginalized communities in the AI development process allows social work organizations and professionals to support more inclusive practices.

This strategy nurtures ethical AI practices and helps ensure that AI tools are better suited to address the diverse needs and experiences of various client groups and communities. AI designers can incorporate diverse perspectives and insights to understand better and serve the unique requirements of a broad range of individuals. This inclusive approach improves the overall quality of AI applications and helps build trust and foster collaboration between AI developers and the communities they serve. Ultimately, this leads to more effective, equitable, and beneficial AI solutions.

Privacy, Confidentiality, and Transparency

When incorporating generative AI into social work practice, safeguarding client privacy and confidentiality is crucial. AI-powered tools for data analysis, report generation, or client communication must comply with stringent data protection standards and relevant privacy laws. Secure storage and transfer, and anonymization are essential to deter unauthorized access. Social workers should review AI-generated content to uphold confidentiality and professional ethics.

Transparency is an essential ethical aspect when using generative AI. Social workers need to communicate openly with clients and stakeholders regarding the potential impact of AI-powered tools. Transparency involves explaining how the AI contributes to care, discussing benefits and limitations, and addressing concerns. Moreover, developers and social work organizations must strive for clarity in AI algorithm development and usage. They should aim for AI tools with transparent decision-making processes that enable social workers and stakeholders to understand and evaluate them. This approach encourages responsible, and ethical AI use while fostering trust among social workers, clients, and the community.

Next steps

As we’ve explored in this introductory article, generative AI, specifically large language models, can fundamentally transform social work students’ educational experience and enhance the practice of professionals in the field. From mental health services and program administration to policy analysis, program evaluation, and community organizing, AI technologies offer innovative tools and resources that can augment the capabilities of social workers.

To effectively utilize AI in social work, we must focus on combining content expertise with technical skills. While AI-powered tools can perform routine tasks more efficiently than social workers, it is crucial to understand that AI cannot replace social workers. Instead, AI can assist social workers by handling mundane tasks and freeing their time to focus on issues requiring human intervention. In other words, social workers have unique skills that AI lacks.

Students and professionals alike must approach these technologies responsibly and ethically. Developing a deep understanding of large language models, their training data, and limitations and honing essential skills like prompt engineering can help users harness the power of AI while mitigating potential inaccuracies and biases. I’ll cover some of these topic areas in subsequent articles in this series.

I’m a Professor of Social Work at the University of Michigan, interested in preparing students and helping non-profit organizations use data and information technologies to work smarter, not harder. Follow me if you are interested in learning more about these topics.


Generative AI for social work students- Part I was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

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