Comparative Efficacy of AI LLMs in Clinical Social Work

ChatGPT-4, Gemini, Copilot

Bibliographic Data

ID6448623
AuthorsHacer Taşkıran Tepe (0000-0002-1500-6582, Bilecik University, corresponding author), Hüsnünur Aslantürk (0000-0002-8174-4970, Department of Social Work, Anadolu University, Eskişehir, Türkiye)
Year2025
Volume35
Issue8
Pages968-980
Publication date2025-01-17
Open AccessYes
TypeARTICLE
VenueResearch on Social Work Practice (JOURNAL)
Journal identifiersISSN: 1049-7315 • E-ISSN: 1552-7581
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/10497315241313071
OpenAlexW4406527073
LanguageEN
Citations received1
References cited24

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

Clinical social work · Political science · Self-efficacy · Work (physics · Artificial Intelligence in Healthcare and Education · Engineering · Healthcare cost, quality, practices · Hip and Femur Fractures · Law · Psychology · Social Psychology · Social Work

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