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Methodological Trend in Child Social Work Research in the Age of AI: a Five-Year Review

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  • Wen, Hao

Abstract

No study has systematically examined how data analysis methods have evolved in child and family social work research. This study analyzed 6,956 articles published between 2021 and 2025 in five SSCI-indexed journals (Child Abuse & Neglect, Child Maltreatment, Children and Youth Services Review, Family Relations, and Child and Adolescent Social Work Journal) to identify methodological trends coinciding with the emergence of generative artificial intelligence. Article abstracts were classified by study design and analytical approach using LLM-assisted content analysis, validated against human coding (Cohen's κ = .92 for study design, κ = .85 for analytical methods). Logistic regression models controlling for journal revealed significant increases in network analysis (OR = 1.851), machine learning (OR = 1.332), mediation analysis (OR = 1.075), and qualitative methods (OR = 1.078), alongside declines in bivariate analysis (OR = 0.954) and logistic regression (OR = 0.920). Pre- versus post-ChatGPT comparisons and sensitivity analyses confirmed these patterns. Findings document a shift toward more complex quantitative methods and growing methodological diversification, with implications for doctoral training, editorial standards, and the responsible use of AI in research.

Suggested Citation

  • Wen, Hao, 2026. "Methodological Trend in Child Social Work Research in the Age of AI: a Five-Year Review," SocArXiv 7j3zx_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:7j3zx_v1
    DOI: 10.31219/osf.io/7j3zx_v1
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