Comparative Efficacy of AI LLMs in Clinical Social Work: ChatGPT-4, Gemini, Copilot

Abstract

Purpose: This study examines the comparative efficacy of three AI large language models (LLMs)—ChatGPT-4, Gemini, and Microsoft Copilot—in clinical social work. Method: By presenting scenarios of varying complexities, the study assessed their performance using the Ateşman Readability Index and a Likert-type accuracy scale. Results: Results showed that Gemini had the highest accuracy, while Microsoft Copilot excelled in readability. Significant differences were found in accuracy scores (p =.003), although readability differences were not statistically significant (p =.054). No correlation was found between case complexity and either accuracy or readability. Discussion: Despite the differences, none of the models fully met all accuracy standards, indicating areas for further improvement. The findings suggest that while LLMs offer promise in social work, they require refinement to better meet the field's needs. © The Author(s) 2025.