An educator’s route into AI literacy

I am an AI literacy educator and Lecturer at Nanyang Technological University in Singapore.

Across 36 CC0007 tutorial classes, I have taught approximately 1,700+ undergraduates. My teaching focuses on grounded inquiry, problem framing, evidence evaluation, assessment and responsible human–AI collaboration.

I am particularly interested in how universities can prepare students to use generative and agentic AI without weakening the deep knowledge, productive practice and accountability through which expertise develops.

I also build small AI systems because working prototypes make theoretical assumptions concrete. They force a practical question:

What should the AI do, what should the human continue to do, and how should the system shape that relationship?

I think about AI through a simple lens:

The practical effect of AI depends not only on what a model can do. It also depends on how learning and work are structured around it:

• what people delegate;
• what they continue to practise themselves;
• what they verify;
• where judgement remains essential;
• how responsibility is allocated; and
• which capabilities are strengthened or allowed to atrophy.

That is why I am less interested in AI adoption as an end in itself than in the design of effective human–AI systems.

I hold a PhD in Management (Organisational Behaviour) from Nanyang Technological University, an MSc in Leadership and Strategy through the Sloan Fellowship at London Business School, and a BSc in Economics from the University of Porto.

Before academia, I spent roughly fifteen years in financial markets and later worked on international business and advisory projects.

That background continues to shape how I think about AI: through uncertainty, incentives, adaptation, evidence quality, risk and decision-making under pressure — not through technology in isolation.

The movement into AI and higher education is therefore less of a break than it might initially appear. Across markets, organisations and learning environments, the underlying problem is similar: people must make good decisions when information is abundant, incomplete, rapidly changing and increasingly mediated by technology.

I treat imagination as an operation on existing reality rather than creation from nothing. New systems begin by recombining available materials, knowledge, constraints and technical capabilities into a possibility that does not yet have a working form.

That is why prototyping matters to my work. An idea can be intellectually coherent and still fail when it meets evidence, users or institutional constraints. A prototype turns an imagined arrangement into something that can be inspected, tested and revised.

In both teaching and system design, the movement is the same: frame a possibility, ground it in what is known, make it concrete, and learn from the encounter with reality.

Augmented Minds is the writing and applied-work imprint of this site.

It brings together essays, prototypes and teaching experiments around one proposition:

Intelligent systems are most valuable when they expand human capability and responsibility rather than simply remove humans from the loop.

The emphasis is therefore on augmentation rather than automation for its own sake — and on designing environments in which people can think, learn and decide better with AI.