I’m Hugo Martins, PhD, AI literacy educator and Lecturer at Nanyang Technological University in Singapore.
Applied and interdisciplinary AI-literacy curriculum, assessment and Project-Based Learning (PBL) for an AI-enabled world. My work connects Project-Based Learning (PBL), interdisciplinary adaptive expertise and responsible human–AI work: when AI can generate plausible outputs, how should learning environments make human reasoning, evidence and responsibility visible?
Teaching portfolio · Selected work · Writing · CV · LinkedIn
- 36 CC0007 tutorial classes and counting
- 1,700+ undergraduate students
- Curriculum and assessment practice in CC0007 Science & Technology for Humanity, part of NTU’s interdisciplinary core curriculum
My teaching develops six connected capabilities:
- grounded inquiry and source provenance;
- problem framing before solution generation;
- critical evaluation of AI outputs;
- interdisciplinary synthesis;
- documented human intervention;
- accountable human–AI collaboration.
Case study: AI Literacy in the NTU Core Curriculum
I treat AI literacy as more than tool fluency. Students progress through a structured learning architecture:
| Stage | Learning design | Evidence of learning |
|---|---|---|
| Foundational readiness | Curated preparation and source-grounded research | Students establish technical competence and build a defensible evidence base |
| Facilitated application | Hands-on inquiry, critique and iterative work with AI | Students compare outputs, surface assumptions and improve reasoning |
| Applied mastery | Interdisciplinary project work and individual reflection | Students document where human judgement redirected, constrained or rejected AI |
This approach connects AI use to adaptive expertise: framing ill-structured problems, judging evidence, handling exceptions, integrating perspectives and directing human–AI work responsibly.
Read the adaptive-expertise framework
The repositories below are supporting evidence of applied practice. They are prototypes for exploring pedagogy, reasoning and interaction design—not claims of production software maturity.
| Prototype | Educational purpose | Demonstrates |
|---|---|---|
| Ask Julia | Evidence-grounded conversational knowledge exploration | Retrieval grounding, source-aware answers and voice interaction |
| Critical Thinker | Structured evaluation of arguments and evidence | Critique scaffolding, analytical perspectives and report generation |
| Six Thinking Hats | Collaborative examination of facts, risks, benefits and alternatives | Parallel thinking and group reasoning workflows |
| Generative Lesson Planner | Instructor-led lesson and activity design from supplied materials | Retrieval-augmented preparation with educator control |
| RAG Avatar | Document-grounded conversational learning | Retrieval, document Q&A and interface experimentation |
- How should universities assess judgement when AI can generate polished outputs?
- Which forms of productive difficulty are necessary for developing expertise?
- How can students learn to supervise agentic workflows rather than passively consume answers?
- How should curriculum and work design distribute responsibility across people, AI agents and institutions?
My work brings together higher education, organisational behaviour and applied AI. I hold a PhD in Management (Organisational Behaviour) from NTU, an MSc in Leadership and Strategy from London Business School through the Sloan Fellowship, and a BSc in Economics from the University of Porto.
Before academia, I worked for approximately fifteen years in financial markets and later in business and strategic advisory roles. That experience continues to shape how I think about judgement under uncertainty, incentives, adaptation and decision-making under pressure.
Views and projects presented here are my own.