AI and Cultural Context: An Empirical Investigation of Large Language Models' Performance on Chinese Social Work Professional Standards
Zia Qi, Brian E. Perron, Miao Wang, Cao Fang, Sitao Chen, Bryan G., Victor

TL;DR
This study evaluates how well large language models from Chinese and Western origins understand and apply Chinese social work principles, revealing cultural biases and highlighting challenges in achieving cultural competence.
Contribution
It provides an empirical comparison of Chinese and Western LLMs' performance on Chinese social work standards, emphasizing cultural biases and competence limitations.
Findings
Chinese models perform better in jurisprudence
Both groups show cultural biases in gender and family topics
Models struggle with culturally specific interventions
Abstract
Objective: This study examines how well leading Chinese and Western large language models understand and apply Chinese social work principles, focusing on their foundational knowledge within a non-Western professional setting. We test whether the cultural context in the developing country influences model reasoning and accuracy. Method: Using a published self-study version of the Chinese National Social Work Examination (160 questions) covering jurisprudence and applied knowledge, we administered three testing conditions to eight cloud-based large language models - four Chinese and four Western. We examined their responses following official guidelines and evaluated their explanations' reasoning quality. Results: Seven models exceeded the 60-point passing threshold in both sections. Chinese models performed better in jurisprudence (median = 77.0 vs. 70.3) but slightly lower in…
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Taxonomy
TopicsSocial Work Education and Practice · Mental Health via Writing · Legal Education and Practice Innovations
