Paid Workshop E1

The Future of AI in Social Work Is Small, Local, and Agentic: A Skill Development Roadmap

社會工作中的 AI 未來：小型化、在地化與代理式——技能發展路徑圖

Instructor: Dr. Brian E. Perron (Professor, University of Michigan)

Date, Time, & Location: Sunday, June 7, 2026 | 9:00 – 12:00 (3 hours) | HG01

Language: English

[Introduction] "Agentic AI" is one of the most hyped terms in technology today, but beneath the buzz is a genuinely transformative capability: AI systems that can reason through multi-step tasks, use tools, and act autonomously on a user's behalf. This workshop cuts through the hype and demonstrates what agentic AI actually looks like in practice. The session also challenges the assumption that useful AI requires expensive cloud-based systems. Participants will learn how small, open-weight models running on personal hardware can protect client privacy, reduce costs, and perform reliably on domain-specific tasks. The workshop provides a practical roadmap for skill development, covering how AI models work, how to evaluate their fitness for specific tasks, how to design agentic workflows, and how to clearly describe a practice need so that AI tools can help build a working solution without programming experience. Participants will leave with a concrete framework for applying these ideas in their own practice and research.

[Bio] Dr. Brian E Perron is a Professor at the University of Michigan School of Social Work and Co-Director of the Child and Adolescent Data Lab. His research focuses on using artificial intelligence to advance social work research and practice. He has developed extensive expertise in using small and large language models to organize, classify, and analyze large volumes of text-based data, including case records, policy documents, and academic literature. His current work involves building AI-powered systems that make policy information more accessible to practitioners, evaluating how well different language models perform on tasks specific to social work, and using AI to map trends and patterns across decades of published research. He is committed to deploying AI responsibly, with particular attention to data privacy, the integration of human judgment into AI-assisted workflows, and ensuring that these tools are grounded in the realities of social work practice.