Track Co-Chairs
Ling Zhao
Professor
lingzhao@mail.hust.edu.cn
Huazhong University of Science & Technology
Zilong Liu
Professor
Zilonglord@126.com
Dongbei University of Finance & Economics
Aihui Chen
Associate Professor
aihui@tju.edu.cn
Tianjin University
Brief Introduction
The emergence of artificial intelligence (AI)—particularly the recent surge of generative AI—is transforming how humans interact with digital systems. As AI systems become more adaptive, creative, and context-aware, human–AI interactions are shifting from traditional “use” patterns toward cooperation, co-creation, and shared decision-making. Understanding this transformation requires an integrated perspective that examines not only user behaviors and experiences, but also the underlying psychological, cognitive, and societal mechanisms shaping these interactions.
This track seeks research that advances our theoretical and empirical understanding of how humans perceive, experience, and engage with AI, and how we can better design, guide, and govern responsible Human–AI Interaction.For example, how people establish and calibrate trust toward generative AI, how cognitive and affective mechanisms influence their interaction behaviors, and how design interventions can promote transparency, control, and user agency in AI-mediated contexts. We welcome conceptual, empirical, and design-oriented research using quantitative, qualitative, experimental, or mixed methods. This track aims to foster an interdisciplinary dialogue at the intersection of information systems, human–computer interaction, and behavioral science—ultimately to better understand, design, and guide Human–AI Interaction, especially in the age of generative AI.
Topics
– Usage, adaptation, and appropriation of AI and generative systems
– User experience, satisfaction, and emotional engagement with various AI system
– Human–AI collaboration and co-creation in work, learning, and creativity
– Trust, reliance, and control perception in Human–AI Interaction
– Cognitive biases, user mental models in HAI
– Human-centered and value-sensitive design for AI systems
– Interaction design for transparency, explainability, and adaptivity
– Conversational and multimodal interfaces for generative AI
Associate Editors
Yicheng ZHANG
Associate Professor
yiczhang@hust.edu.cn
Huazhong University of Science & Technology
Jifeng MA
Assistant Professor
jfma@wtu.edu.cn
Wuhan Textile University
Yuni LI
Assistant Professor
liyuni@hbue.edu.cn
Hubei University of Economics