Artificial Intelligence In Qualitative Analysis: Opportunities, Challenges, and Future Directions for Social Work Research

Over the past decade, social science researchers have increasingly explored artificial intelligence (AI) for data analysis and predictive modeling. At the same time, qualitative research remains essential for understanding the lived experiences of individuals, practitioners, and communities, especially in social work, where context and meaning matter deeply. Bringing AI into qualitative research offers exciting possibilities, but also raises concerns that deserve careful attention.

Traditional qualitative methods, such as manual coding and thematic analysis, are essential for capturing rich, nuanced perspectives. However, they can be time- and labor-intensive, particularly when working with large datasets. New AI tools, especially large language models (LLMs), are beginning to support some of these early stages, such as generating initial codes, identifying patterns, and organizing large amounts of text. LLMs are trained on vast amounts of language data and can detect patterns in how words and ideas are used. These tools are now integrated into qualitative software like ATLAS.ti and NVivo through “AI-assisted” or “intentional” coding.

At the same time, AI use in qualitative research is still relatively new, and has sparked debate on its promise and risks. While some view AI as a way to improve efficiency and open new analytic possibilities, others raise concerns. Because AI relies on patterns in data rather than true interpretation, it may miss important context, cultural meaning, or ethical nuance. This can lead to bias or misinterpretation, especially when working with sensitive topics or data from marginalized communities. In social work research, where findings often inform real-world policies and practices, these risks are particularly important to consider.

This roundtable brings together early-, mid-, and senior-career scholars working at the intersection of social work and data science to reflect on these opportunities and challenges. Panelists will share experiences using AI in qualitative research, including what works, what raises concerns, and what they are still figuring out. Discussion will focus on comparing AI-generated codes to human coding, how to maintain rigor and transparency, the ethical use of AI, and navigating interdisciplinary research and publication spaces. Panelists will also reflect on training and mentorship, as well as how to integrate AI into research teams and classrooms. Finally, this roundtable will highlight the importance of engaging communities in reviewing and validating AI-assisted analysis to ensure findings remain grounded in lived experiences and aligned with ethical commitments.

This session will be highly interactive, engaging participants in critical dialogue around the future of qualitative inquiry in the age of AI. The discussion will examine pressing questions: How can AI enhance qualitative research without compromising depth, context, and human meaning? What does responsible, ethical, and community-informed use of these tools look like in practice? By fostering rigorous discussion and collective reflection, this roundtable aims not only to exchange ideas, but to shape emerging standards, build interdisciplinary collaborations, and advance a shared vision for the ethical integration of AI in qualitative social work research. Ultimately, this session seeks to move the field forward, ensuring that innovation strengthens, rather than diminishes, the core values of social work research.