Clinical Social Workers’ Perceptions of Large Language Models in Practice: Resistance to Automation and Prospects for Integration

PURPOSE: This research explores clinical social workers' perceptions of the usefulness of generative artificial intelligence (AI) in clinical practice, with a particular focus on large language models (LLMs). MATERIALS AND METHODS: This qualitative reflexive thematic analysis explored the interviews of 21 clinical social workers and how they experience their work in the context of growing LLM use. Participants shared their perceptions and experiences with LLMs following a collaborative case consultation exercise using ChatGPT and a video demonstration of a client using ChatGPT. RESULTS: Social work practitioners described both benefits and concerns with LLM use in their practice. Two overarching themes emerged: (1) factors that enhanced social workers' perceived usefulness of LLMs in clinical practice, including support for administrative tasks and client engagement, and (2) factors that diminished perceived usefulness, such as concerns about confidentiality, loss of nuance, and limitations in conveying empathy and contextual understanding. DISCUSSION: Practitioners shared that they are using LLMs as idea generators in clinical work, while simultaneously expressing concern about the quality of information and the need for a human‑centered approach. They also noted that their decision to adopt LLMs is shaped by professional ethics and relational values, reflecting a preference for augmentation rather than full automation to preserve therapeutic depth and client wellbeing. CONCLUSION: Future AI implementation should focus on practitioner training and clear ethical guidelines to support responsible integration of LLMs. Ongoing evaluation will be essential to ensure these tools enhance clinical practice without compromising the therapeutic relationship or core social work values.