Empowering Social Service with AI: Insights from a Participatory Design Study with Practitioners

TL;DR

This study investigates how Generative AI can support social service practice through a participatory design process. In stage one, SSPs co-designed a GenAI prototype to assist documentation, assessment, and intervention planning; in stage two, practitioners tested the tool with real case data and reflected on outputs during contextual inquiry. The findings reveal clear opportunities for AI to reduce manual coding, aid case formulation, and enhance supervision, while highlighting risks around privacy, overreliance, and the challenge of embedding local-context knowledge. The authors discuss design guidelines and draw cross-domain insights, arguing that GenAI should augment rather than replace the human touch in social service. They advocate longitudinal studies and workflow optimization to maximize benefits while maintaining client safety and professional judgment.

Abstract

In social service, administrative burdens and decision-making challenges often hinder practitioners from performing effective casework. Generative AI (GenAI) offers significant potential to streamline these tasks, yet exacerbates concerns about overreliance, algorithmic bias, and loss of identity within the profession. We explore these issues through a two-stage participatory design study. We conducted formative co-design workshops (\textit{n=27}) to create a prototype GenAI tool, followed by contextual inquiry sessions with practitioners (\textit{n=24}) using the tool with real case data. We reveal opportunities for AI integration in documentation, assessment, and worker supervision, while highlighting risks related to GenAI limitations, skill retention, and client safety. Drawing comparisons with GenAI tools in other fields, we discuss design and usage guidelines for such tools in social service practice.

Figures (3)

• Figure 1: Prototype AI Tool
• Figure 2: Workshop participants
• Figure 3: Notes generated by participants