Studying the conditions in which people learn.
Our research examines learning as a cognitive, social, technological, and institutional practice. We combine conceptual work, empirical inquiry, design research, and field collaboration.
Interdisciplinary by design
The questions we face do not fit neatly inside one discipline. We work across learning sciences, cognitive science, education, human–computer interaction, philosophy, design, and the social sciences.
From theory to fieldwork
We connect foundational ideas to real settings: classrooms, workplaces, communities, and digital platforms.
Useful knowledge
We make research available through publications, prototypes, convenings, and practical resources.
Human OS
Treating the human inner system — attention, judgment, learning — as a designable architecture, not something left to chance.
- What does it mean to engineer your own cognitive architecture deliberately, the way machine learning systems are engineered?
- Which human capacities benefit from AI support, and which are degraded by it?
- Can principles drawn from building machine learning systems be applied, in reverse, to human development?
Epistemic Agency in AI-Augmented Organisations
How organisations keep, or quietly lose, the capacity for independent judgment as AI becomes part of how they reason and decide.
- What does epistemic agency look like inside a team, and how would you know if it were eroding?
- Where does AI-assisted reasoning strengthen collective judgment, and where does it quietly substitute for it?
- What would a practical audit of an organisation's epistemic health include?
Knowledge Structures for AI Reasoning
Restructuring how knowledge is captured and organised so AI systems can reason with it, not just retrieve it.
- What separates a structure built for retrieval from one built for reasoning?
- Can organisational knowledge be broken into typed, linked objects without losing the context that made it useful?
- What is lost — in distortion or nuance — when knowledge is compressed into a form a machine can use well?
Tacit Knowledge and Leadership Learning
How tacit knowledge — the kind practitioners carry but rarely articulate — gets surfaced, taught, and passed on, particularly through narrative and dialogue.
- Can an interview itself be a knowledge-creation event, not just a method for collecting what already exists?
- What can film and narrative teach about leadership that direct instruction cannot?
- How should leadership education change once tacit knowledge is treated as a legitimate subject of study in its own right?
Decision-Making in Human-AI Collaboration
Studying how the quality of a decision changes when people reason through it together with AI, rather than relying on either party alone.
- What changes in the quality, speed, or ownership of a decision when it is made through human-AI collaboration?
- How do people build and calibrate trust in an AI reasoning partner across a sequence of real decisions?
- What does good collaborative decision-making actually look like in practice, and how would you measure it?