Research

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

• How should learning be conceptualized when human–AI interaction becomes part of education, work, and society?

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

• What human capabilities are required for productive human–AI interaction, and how do those capabilities develop over time?

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

• How should learning and teaching be designed for productive human–AI interaction?
• How should assessment evaluate durable human learning and synergistic performance?

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

• What process evidence can distinguish durable human learning from AI-supported performance?
• How can learning be modeled credibly over time?

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

• How can understanding human learning inform responsible next-generation AI technologies?
• How should organizations create fair and inclusive conditions for capability development?