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

被引:0
作者
Tepe, Hacer Taskiran [1 ]
Aslanturk, Husnunur [2 ]
机构
[1] Bilecik S?eyh Edebali Univ, Dept Social Work, Bilecik, Turkiye
[2] Anadolu Univ, Dept Social Work, Eskisehir, Turkiye
关键词
ChatGPT; Gemini; Microsoft Copilot; artificial intelligence; clinical social work; BARD;
D O I
10.1177/10497315241313071
中图分类号
C916 [社会工作、社会管理、社会规划];
学科分类号
1204 ;
摘要
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 Ate & scedil;man 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.
引用
收藏
页数:13
相关论文
共 36 条
  • [1] Evaluating the Sensitivity, Specificity, and Accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard Against Conventional Drug-Drug Interactions Clinical Tools
    Al-Ashwal, Fahmi Y.
    Zawiah, Mohammed
    Gharaibeh, Lobna
    Abu-Farha, Rana
    Bitar, Ahmad Naoras
    [J]. DRUG HEALTHCARE AND PATIENT SAFETY, 2023, 15 : 137 - 147
  • [2] Redefining Healthcare With Artificial Intelligence (AI): The Contributions of ChatGPT, Gemini, and Co-pilot
    Alhur, Anas
    [J]. CUREUS JOURNAL OF MEDICAL SCIENCE, 2024, 16 (04)
  • [3] [Anonymous], 2017, Standards for technology in social work practice
  • [4] A Call to Action on Artificial Intelligence and Social Work Education: Lessons Learned from A Simulation Project Using Natural Language Processing
    Asakura, Kenta
    Occhiuto, Katherine
    Todd, Sarah
    Leithead, Cedar
    Clapperton, Robert
    [J]. JOURNAL OF TEACHING IN SOCIAL WORK, 2020, 40 (05) : 501 - 518
  • [5] Ateman E., 1997, Dil Dergisi, V58, P71
  • [6] Bronfenbrenner U., 1979, ECOLOGY HUMAN DEV, DOI [10.4159/9780674028845, DOI 10.4159/9780674028845]
  • [7] Brown TB, 2020, ADV NEUR IN, V33
  • [8] Cohen J., 2013, STAT POWER ANAL BEHA, DOI [DOI 10.4324/9780203771587, 10.4324/9780203771587]
  • [9] Columbia Center for Teaching and Learning, 2023, Considerations for AI Tools in the Classroom
  • [10] Crawford Joseph, 2023, JOURNAL OF UNIVERSITY TEACHING AND LEARNING PRACTICE, V20, P1