Evidence · The claim ledger
Practitioner surveys & adoption7
Every ledgered claim this site makes in this evidence area, with the sources that ground it — or, for a PAN-simulation-derived claim, the run it comes from. Source keys link back to the full reference lists on the Evidence Registry.
EmpiricalIn a national survey of 1,179 U.S.-based social workers conducted from October 2025 to February 2026 by the University o…
In a national survey of 1,179 U.S.-based social workers conducted from October 2025 to February 2026 by the University of Texas at Austin in collaboration with NASW, 63.5% of respondents reported using AI tools or technologies in their current role.
Sources: borah2026, isbanner2022
Appears on: /pan-lab, /practice/ai-literacy, /what-ai-can-do
EmpiricalIn the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, concerns about data privac…
In the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, concerns about data privacy and security were the most frequently reported challenge to using AI in practice (46.5% of respondents), and an increased focus on client privacy and confidentiality was the most requested improvement to AI tools for social work (50.4%).
Sources: borah2026, isbanner2022
Appears on: /pan-lab, /practice/data-minimization
EmpiricalIn the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, 40.8% of respondents repor…
In the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, 40.8% of respondents reported ethical concerns about relying on AI for decision-making, and overreliance on automated decision-making was among the most frequently cited concerns overall.
Sources: borah2026, pinazohernandis2026
Appears on: /pan-lab
EmpiricalThe 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers describes a gap between AI exp…
The 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers describes a gap between AI exposure and AI preparedness: 26.6% of respondents cited lack of training or understanding of AI technology as a challenge, 53.4% said training on AI tools and effective use would help, and clear guidelines on the ethical use of AI were the most-endorsed need (66.8%).
Sources: borah2026, pinazohernandis2026
Appears on: /pan-lab, /practice/ai-literacy
EmpiricalIn the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, 42.1% of respondents repor…
In the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, 42.1% of respondents reported having no role in decision-making about AI adoption in their workplace; the report concludes most respondents have limited or no control over how AI technologies are selected or implemented within their organizations.
Sources: borah2026
Appears on: /pan-lab
EmpiricalIn the open-ended comments of the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers,…
In the open-ended comments of the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, ethical concerns — prominently including the environmental impact of AI infrastructure — were the most common theme, and the report's first recommendation includes environmental impact among the topics profession-wide ethical guidance should address.
Sources: borah2026, massey2026
Appears on: /pan-lab
EmpiricalDocumentation and administrative tasks consume roughly half of practitioner time: a nationally representative US child-w…
Documentation and administrative tasks consume roughly half of practitioner time: a nationally representative US child-welfare workforce snapshot found caseworkers spend about 54% of the workday (4.3 of 8 hours) on paperwork and documentation, and a UK children's-services review reports staff spending over 50% of their time on case recording, paperwork, and related tasks.
Sources: opre2025, burbidge2022
Appears on: /pan-lab, /what-ai-can-do