Research

CLAIR studies how durable human capabilities can be developed, assessed, and supported when AI becomes part of education.

01 / UNDERSTAND

Conceptual foundations for learning in human–AI interaction

We build the conceptual foundations needed to understand what learning, expertise, and human capability mean when AI becomes part of education, work, and society.

Durable human learning

We distinguish lasting capability development from short-term performance that is only possible with AI support.

Human–AI synergy

We examine when AI strengthens human judgment and agency, and when interaction risks dependency or capability loss.

Capability development over time

We study how human capabilities develop across ages, disciplines, and changing technological contexts.

02 / DEVELOP

Human capabilities for productive human–AI interaction

We investigate the cognitive, metacognitive, motivational, and affective capabilities people need to work with AI productively and retain meaningful human agency.

Cognitive and metacognitive regulation

We study how people plan, monitor, evaluate, and adapt their thinking while learning with intelligent systems.

Motivation, agency, and affect

We examine how confidence, effort, emotion, and perceived control shape productive engagement with AI.

AI literacy and critical judgment

We investigate the knowledge and judgment required to question, verify, and responsibly use AI-generated outputs.

03 / DESIGN

Design for learning, teaching, and assessment

We design educational and professional activities that make human–AI interaction a genuine opportunity for learning rather than a substitute for it.

Learning and teaching with AI

We create learning designs that position AI as a partner for explanation, reflection, practice, and feedback.

Assessment in the age of AI

We develop assessment approaches that recognise both independent human capability and effective human–AI performance.

Authentic disciplinary practice

We connect learning and assessment to real disciplinary and professional practices.

04 / EVIDENCE

Learning analytics and evidence infrastructure

We develop the evidence infrastructure needed to observe learning processes, model change over time, and understand how people regulate human–AI interaction.

Process evidence

We use traces of activity and interaction to reveal how learning unfolds, not only what outcome is produced.

Longitudinal models of learning

We model development over time to identify patterns of growth, transfer, persistence, and productive AI use.

Actionable analytics

We translate evidence into feedback and decision support for learners, educators, researchers, and institutions.

05 / GOVERN

Responsible AI technology and organizational design

We connect evidence about human learning with the design of responsible AI for capability development.

Human-centred AI

We use knowledge about learning and expertise to inform AI systems that support human development, agency, and accountability.

Fair and inclusive participation

We investigate how access, opportunity, and support shape who can benefit from AI.

Organizational conditions

We study the policies and practices institutions need to adopt AI responsibly while investing in human expertise.