Willingness to Use and Barriers to Implementing Artificial Intelligence-Supported Case Management Systems in Social Work: A Quantitative Survey of Social Work Practitioners
DOI:
https://doi.org/10.65150/EP-jsshrs/V2E5/2026-18Keywords:
artificial intelligence, social work, case managemet, technology, digital transformation, self efficacyAbstract
The ongoing digital transformation of human services has positioned artificial intelligence (AI) as a potentially significant resource for social work practice, particularly in the domain of case management. Yet empirical investigation into how social work practitioners evaluate AI-supported systems — and what prevents or facilitates their adoption — remains limited. The present study addresses this gap through a quantitative cross-sectional online survey conducted with N = 97 social work professionals drawn from diverse practice fields, including youth welfare, psychosocial counseling, clinical social psychiatry, refugee support, and disability services. Three purpose-developed Likert-format scales assessed willingness to use AI case management systems (α = .88), perceived benefits (α = .86), and perceived barriers (α = .91). Perceived benefit was the strongest bivariate correlate of willingness (r = .68, p < .001), followed by perceived barriers (r = −.52, p < .001) and technological self-efficacy (r = .47, p < .001). Data privacy concerns showed a substantial negative association with willingness (r = −.49, p < .001). Multiple regression analysis identified perceived benefit (β = .49, p < .001), perceived barriers (β = −.31, p = .002), and technological self-efficacy (β = .24, p = .011) as significant independent predictors of willingness, jointly explaining 58% of its variance (R² = .58, F(5, 91) = 25.07, p < .001). Professional experience was negatively associated with willingness, with experienced practitioners reporting significantly lower AI adoption readiness than early-career colleagues (ANOVA: F(2, 94) = 4.91, p = .009). Social work practitioners recognize the administrative efficiency and documentation relief AI may offer, but express robust concerns about data protection, the adequacy of AI in capturing complex social situations, and the risk of diminished relational practice quality. Targeted digital competence training and the establishment of transparent, participatory AI governance frameworks in social service institutions are identified as critical enablers of ethically grounded AI adoption.
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