Human–AI Interaction and Cross-Disciplinary Impact

Overview

Through user studies, interaction design and computational social science, this area examines how people understand, rely on and challenge AI, and how AI affects professional judgment, work and institutional responsibility. It asks how efficiency can improve while preserving human judgment, traceable evidence and the ability to contest results.

Methods

Human–AI collaboration studies: investigate generative AI in legal fact verification and interface support for effective collaboration, including the CHI 2026 study.

Social-agent interaction design: use scenario, role and persona frameworks to design understandable and contestable agents, including the CHI EA 2025 study.

Computational social science and governance: examine AI’s effects on professional judgment, work and social governance, connecting technical design with institutional analysis.

Cognition and visualization: study language, learning and cognition, using text visualization, social network analysis and explainable AI to support evidence verification.

Public policy and service evaluation: assess AI in public services and professional work, including changing roles and the allocation of responsibility.

Research Questions

When do people rely on or challenge AI?

How can interfaces support evidence verification?

How does AI change professional roles and responsibility?

How can efficiency improve while preserving human judgment?

Resources & Outputs

“Reimagining Legal Fact Verification with GenAI: Toward Effective Human-AI Collaboration” (CHI 2026); “Scenario, Role, and Persona: A Scoping Review of Design Strategies for Socially Intelligent AI Agents” (CHI EA 2025); “AI as skills governance: reconfiguring authority and ethics in China’s TVET reform” (2026). Planned programme: Future of Professional Work.