In 2018, the Family First Prevention Services Act (FFPSA) began requiring child welfare organizations to use and monitor evidence-based practices to receive federal funding. Recently, some organizations have leveraged AI tools designed to record and evaluate workers’ interactions with clients to meet these compliance requirements. With this grant, Mosley and Maschke will conduct a comparative case study of child welfare organizations to explore how workers, managers, and leaders experience and implement AI tools, make sense of their data, and use it to understand research use. The team will collect data by: 1) observing trainings, clinical supervisions, and compliance meetings; 2) interviewing child welfare managers and workers; 3) collecting policy-, product-, and site-specific documents; and 4) interviewing AI tools representatives. Thematic and comparative analyses will shed light on how different sites (e.g., nonprofits, government agencies) and different staff members (e.g., frontline, mid-level, etc.) use and perceive AI tools when implementing federal policy.
How do child welfare agencies use an artificial intelligence (AI) tool to monitor compliance with policy-mandated requirements for the use of research evidence, and how does this practice shape organizational actors’ conceptual use of research evidence?
