Developing human capabilities for productive human–AI interaction.

CLAIR brings together learning sciences, learning analytics, and responsible AI to strengthen human expertise, judgment, and learning.

HKU · Faculty of Education · School of Computing and Data Science

Explore Our Research Meet the Team

Open book composed of words, equations, diagrams, and fields of knowledge

Research priorities

Five connected priorities define CLAIR’s research agenda.

Conceptual foundations for learning in human–AI interaction

CLAIR examines how learning, expertise, and capability development should be understood when AI becomes part of education, work, and society.

Explore

Human capabilities for productive human–AI interaction

This priority investigates how cognitive, metacognitive, motivational, and affective capabilities develop over time and across age levels.

Explore

Design for learning, teaching, and assessment

CLAIR studies how educational and professional activities should be designed for productive human–AI interaction across disciplines, professions, and authentic settings.

Explore

Learning analytics and evidence infrastructure

Learning analytics provides the methodological engine for generating process evidence, modeling learning over time, and examining how people regulate human–AI interaction.

Explore

Responsible AI technology and organizational design

This priority connects evidence about human learning with responsible AI design and fair, inclusive organizational conditions for human capability development.

Explore

Selected publications

View All Publications

YearPublicationVenue

2026

AI Literacy For All: 2nd International Workshop on AI Literacy Education For All

Ruiwei Xiao, Shan Zhang, Xinying Hou, Ying-Jui Tseng, Qianou Ma, Yash Tadimalla, Qing Xiao, Jionghao Lin, Bo Jiang, John Stamper, Kenneth R. Koedinger

AIED'26 International Conference on Artificial Intelligence in Education, 44-50, 2026

2026