Build knowledge
Develop enough disciplinary understanding and source awareness to recognise what matters—and when a fluent answer is wrong.
A more demanding definition
AI literacy is more than tool fluency
It is the capacity to work with AI without surrendering the knowledge, judgement and responsibility that make the work trustworthy.
That capacity matters more as generative systems move from producing answers to participating in research, analysis and multi-step workflows.
Direct the work
Frame the problem, define constraints, choose evidence and decide where human intervention is required.
Defend the result
Test assumptions and trade-offs, document consequential choices and remain accountable for the outcome.
Featured teaching case
AI Literacy in the NTU Core Curriculum
Teaching responsible use of generative and agentic AI within CC0007 Science and Technology for Humanity.
Students move from curated preparation to facilitated inquiry and applied project work. The design makes source provenance, problem framing, critique and human intervention visible rather than treating the final output as sufficient evidence of learning.
- Context
- NTU Interdisciplinary Collaborative Core
- My role
- Tutorial design, facilitation, feedback and assessment practice
- Focus
- Grounded inquiry, judgement and accountable human–AI collaboration
Teaching philosophy
How do we make experts when AI removes the first rung?
The central challenge
Education must deliberately recreate the practice through which knowledge becomes judgement.
Knowledge
Build a domain model
Students need enough substantive understanding to detect omissions, weak evidence and confident error.
Practice
Protect productive friction
Attempts, critique, revision and reflection turn accessible answers into durable capability.
Inquiry
Frame consequential problems
When solutions are abundant, defining the right problem, stakeholders and constraints becomes more valuable.
Evidence
Make performance observable
Dialogue, defence and visible reasoning reveal capability more credibly than a polished artifact alone.
Agency
Increase responsibility
Learners earn autonomy through authentic, interdisciplinary work in which judgement has visible consequences.
Selected work
Practice, frameworks and applied systems
AI Literacy in the Core Curriculum
A three-tier learning architecture that moves students from foundational readiness to facilitated inquiry and applied mastery.
Explore →Adaptive Expertise for Human–AI Work
A framework for problem framing, evidence judgement, exception handling and responsible direction of human–AI work.
Explore →Educational AI Systems
Evidence-grounded and structured-reasoning prototypes that make pedagogical assumptions inspectable.
Explore →Selected writing
AI literacy, expertise and human capability
When AI Removes the First Rung: How Universities Can Still Build Expertise
AI can perform much of the clean, checkable junior work through which people once developed judgement. If expertise no longer emerges as a by-product of production, universities must deliberately rebuild the practice, feedback and accountable performance that make it possible.
Read essay →The Real Advantage Is Not Having Better AI. It Is Building a Better Human-AI Service System.
The successful AI adopter is not necessarily the one that automates the most. It is the one that redesigns work most intelligently. This is likely the trap many organizations will fall into. Cost savings appear immediately in spreadsheet projections. Better service consistency, customer experience, satisfaction, and loyalty are slower to appear, but they accumulate over time. And this is exactly where organizational design affects growth and profitability.
Read essay →From Chatbots to Agents: Why AI-Readiness Is About Judgment, Not Prompting Tricks
A chatbot mainly responds. An agent can work towards an outcome. It can break a task into steps, use tools, retrieve information, check progress, and continue until a goal is reached. In simple terms, AI is becoming less like a clever reply machine and more like a system that can participate in execution. That is why this is not only a technology story. It is also a work story, a learning story, and an institutional story.
Read essay →Academic-practitioner profile
Higher education, organisational behaviour and applied AI
My work is grounded in a PhD in Organisational Behaviour, higher-education and adult-learning qualifications, and practical experience designing AI-enabled learning at scale.
My focus is how curriculum, assessment and applied interdisciplinary learning can preserve judgement and human responsibility as AI becomes more capable.