Curriculum architecture
Align AI literacy with programme outcomes, disciplinary knowledge and a deliberate progression from guided use to independent judgement.
Teaching portfolio
Teaching AI literacy at university scale
I design learning that helps students use generative and agentic AI without outsourcing the knowledge, judgement and responsibility that make their work valuable.
42tutorial classes1,700+undergraduates4consecutive semesters≈4.3/5average student feedback
Teaching position
AI literacy is a curriculum of judgement
Tool familiarity is useful, but it is not enough. Students need to understand the domain, formulate consequential questions, evaluate evidence, supervise AI-supported work and remain answerable for the result.
AI can make performance easier. Education must still make expertise possible.
What I design
From institutional ambition to observable student capability
Assessment redesign
Assess the quality of inquiry, evidence choices, human intervention and defence—not only the polish of an AI-assisted output.
Learning innovation
Turn emerging tools into teachable workflows with clear roles for the learner, the educator and the AI system.
Current practice
AI literacy in NTU’s Core Curriculum
I teach CC0007 Science and Technology for Humanity at Nanyang Technological University. The course sits within the Interdisciplinary Collaborative Core and provides a demanding setting for teaching inquiry, evidence and responsible AI use across a highly diverse undergraduate population.
Public reporting on NTU’s AI initiative identifies Science and Technology for Humanity as the mandatory course in which responsible AI and AI-agent skills are taught. My role is to translate that institutional context into tutorial design, facilitation, feedback and assessment practice.
Capability model
What students must be able to do
Six connected capabilities turn AI use from a shortcut into a disciplined form of inquiry and work.
Build knowledge
Develop disciplinary understanding and source-grounded knowledge before asking AI to extend it.
Frame problems
Define meaningful questions, stakeholders, system boundaries and what a plausible answer may omit.
Evaluate evidence
Trace provenance, test credibility, identify contradictions and reason under uncertainty.
Work with AI
Delegate deliberately, design prompts and agents, and supervise multi-step work rather than accept output passively.
Exercise judgement
Weigh trade-offs, ethics, feasibility and context when several defensible responses remain possible.
Remain accountable
Document intervention, defend decisions and retain responsibility for conclusions and consequences.
Learning architecture
Scaffold toward agency
01
Knowledge before delegation
Curated pre-learning establishes baseline concepts, tool fluency and the limits of generative systems. Students learn what they need to know before they can evaluate what AI produces.
02
Practice with visible reasoning
In-class inquiry, cases and critique make prompting, source selection, verification and human intervention discussable. Feedback focuses on the quality of the reasoning process.
03
Judgement in authentic work
Project work asks students to address ill-structured societal problems, integrate perspectives and defend an AI-enabled response in terms of evidence, ethics, feasibility and likely impact.
Assessment
Make the human contribution visible
When polished output is cheap, assessment must reveal how the student arrived there. I design for observable problem framing, source decisions, critique, human intervention, trade-offs and individual reflection.
- Grounded inquiry: students curate a multi-perspective source base and show how claims connect to evidence.
- Authentic project work: teams develop and communicate an AI-enabled response to a real societal problem.
- Defence and reflection: students explain their decisions, limitations and intellectual development.
- Accountable AI use: intervention records make verification, redirection and human judgement inspectable.
Preparation
Academic and professional foundations
My teaching combines organisational-behaviour research, higher-education practice, adult learning and applied AI development.
- PhD in Management (Organisational Behaviour) — Nanyang Technological University
- Certificate in Teaching and Learning in Higher Education — National Institute of Education
- Advanced Certificate in Learning and Performance — Institute for Adult Learning Singapore
- Advanced Certificate in Data Science and Artificial Intelligence — NTU PACE
- Professional Certificate in Applied Artificial Intelligence — Republic Polytechnic / AI Singapore / Microsoft
Academic and learning innovation
Building serious AI capability in higher education
I welcome conversations about AI-literacy curriculum, assessment redesign and responsible human–AI learning in Singapore, Saudi Arabia, the UAE and Europe.
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