About Nizam Kadir
I am a researcher-practitioner, full-stack developer, author and educator working at the intersection of AI pedagogy, learner modelling and multi-agent LLM orchestration. As a doctoral researcher at the Singapore University of Technology and Design (SUTD), I study how educational AI can make transparent, inspectable teaching decisions while keeping educators and learners in control.
My work centres on Machine Pedagogical Intelligence: systems designed not merely to generate fluent answers, but to select and justify appropriate instructional actions. I translate that research into the LearnAdapt ecosystem, including PedOS, Agentic Studio and purpose-built applications for tutoring, assessment, critique and workplace learning.
- Research: Machine Pedagogical Intelligence, adaptive learning, learner modelling, agentic and multi-agent AI.
- Building: LearnAdapt, PedOS, Agentic Studio, Archie Math, CALLIOPE, Critsly, ClioMake and LearnAdapt Praxis.
- Teaching: Computational Data Science at SUTD, connecting data science and AI engineering with human-centred educational practice.
- Experience: More than 20 years across education and technology in the public and private sectors.
Explore my research and publications, applications and enterprise work, and books.
Media & Press
Selected independent coverage and media references to my work in Machine Pedagogical Intelligence and educational AI.
PRESS FEATURE · 3 OCTOBER 2026
Berita Minggu / Berita Harian
“Penyelidik raih anugerah bangun teknologi bantu pelajar guna AI”
A profile by Ainin Alfiyani Jailani on the 2026 DBCS Seed Award, Machine Pedagogical Intelligence, and educational AI designed to support understanding and independent learning.
PODCAST REFERENCE · 20 AUGUST 2026
EdTechnical / BOLD
“Why chatbots aren’t the future of educational AI”
The official episode notes include my AIED 2026 ES-LLMs paper, From Untamed Black Box to Interpretable Pedagogical Orchestration, in the further-reading list. I was not a guest; the listing is a media reference, not an independent review or endorsement of the paper’s findings.
Books and writing
I am the author of Machine Pedagogical Intelligence, Teaching Machines How to Learn: Artificial Intelligence Made Easy, and Co-Designing Educational AI. Visit the Books page for details.
Connect and collaborate
I welcome conversations with researchers, educators, institutions and builders working on interpretable educational AI, learner modelling, co-design, governed agentic systems and evidence-aware learning applications.