What it is
Many workers use AI tools before anyone teaches them how. In the national survey cited below, most of the social workers surveyed used AI tools in their role. About a quarter named a lack of training or understanding as a challenge. Clear guidelines for ethical use were the need they endorsed most. AI literacy closes that gap with plain instruction: what the tool is for, how it fails, and which information must never be pasted into it.
What it pushes on in the Lab
In the Lab, this lever holds down operator deference drift, because people who know the tool's limits rely on it less blindly. It also weakens the pathway by which people paste client data into an unsanctioned tool.
You can also pull this lever at a strong tier, which costs more. At the strong tier, each effect below the Lab's strongest setting pushes harder.
In the modes that offer aiming, you can aim this lever at particular parts and pathways of a network. Otherwise it applies to the whole network.
Its pattern in the Practice Library
The Practice Library describes the pattern behind this lever:AI literacy & boundary rules
The pressures it answers
No pressure page lists this lever among the levers that answer it.
Where you can pull it
Networks in the Lab that offer this lever:1
The evidence behind its effects
The Lab cites these claims from the evidence registry for this lever's effects.
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%).[2]
Borah, E., Meyerhoff, J., Al-Turk, A., Gower, K., & Mastryukova, A. (2026, June). Use of artificial intelligence in social work practice: Findings and recommendations from a national survey. Moritz Center for Societal Impact, Steve Hicks School of Social Work, The University of Texas at Austin. https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
Appears in: Evidence correction (2026)
Topics: human-ai-interaction, social-work
Pinazo-Hernandis, S., & Carcavilla-Gonzalez, N. (2026). Are future social workers ready for AI? Fears, barriers, and learning needs in higher education. Social Work Education. https://doi.org/10.1080/02615479.2026.2631708
doi.org/10.1080/02615479.2026.2631708
Appears in: Evidence reverification (2026)
Topics: human-ai-interaction, social-work
AI literacy — the knowledge and skills required to understand, use, and critically evaluate AI systems — has been proposed as a core competency for social work, relevant even to practitioners who never directly use AI tools.[†]
Ahn, E., Choi, M., Fowler, P., & Song, I. H. (2025). Artificial intelligence (AI) literacy for social work: Implications for core competencies. Journal of the Society for Social Work and Research, 16(1), 9-26. https://doi.org/10.1086/735187
Appears in: National survey report (2026); Paramerge authored research
Topics: social-work
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.[2]
Borah, E., Meyerhoff, J., Al-Turk, A., Gower, K., & Mastryukova, A. (2026, June). Use of artificial intelligence in social work practice: Findings and recommendations from a national survey. Moritz Center for Societal Impact, Steve Hicks School of Social Work, The University of Texas at Austin. https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
Appears in: Evidence correction (2026)
Topics: human-ai-interaction, social-work
Isbanner, S., O'Shaughnessy, P., Steel, D., Wilcock, S., & Carter, S. (2022). The Adoption of Artificial Intelligence in Health Care and Social Services in Australia: Findings From a Methodologically Innovative National Survey of Values and Attitudes (the AVA-AI Study). Journal of Medical Internet Research, 24(8), e37611. https://doi.org/10.2196/37611
Appears in: Evidence reverification (2026)
Topics: human-ai-interaction, public-benefits