From Engineering Intelligence to Human-Machine Collaboration: Two Talks on AI and Education

wendykwan3
7 days ago
3 min read

(by Ricky Lui) As a committee member of the Hong Kong Association for the Advancement of Science and Technology (HKAAST), I was delighted to attend the 2026 World Young Scientists Greater Bay Area Frontier Dialogue, which HKAAST co-organized. It is always a privilege to sit in a room where scientists, engineers, and educators from across the Greater Bay Area compare notes on where AI is actually taking us — and this time, two speakers approached the same question from different ends, one from engineering and one from mathematics, yet interestingly we can see some common threads on what AI should mean for education.

【 Image gallery: photos from the Frontier Dialogue 】

Ir. Ts. Prof. Dr Chee-Fai Tan — a Malaysian engineering leader who serves as Professor and Deputy Vice-Chancellor at Kuala Lumpur University of Science and Technology (KLUST), and Vice President of the Institution of Engineers Malaysia — argued that AI should not be judged by how advanced the models are, but by how well intelligence is applied to real communities and human progress. He traced AI's progression from automation and prediction, through generative AI, to what he called "engineering intelligence" — AI embedded into systems for scalable impact. Translated into an education context, the message is uncomfortable but useful: schools do not simply need more AI tools. They need AI that improves teaching, learning, decision-making, and student support in practical ways — moving beyond chatbots and content generation toward AI that supports curriculum delivery, school operations, personalized learning, and stronger connections between educators, industry, and society. His closing point framed this as a collaboration challenge rather than a technology one: to thrive, our future generation needs to connect knowledge, people, systems, and purpose.

Prof. Michael K. Ng of Hong Kong Baptist University began somewhere very different — with the unit distance conjecture, first raised by Erdős in 1946 and disproved in May 2026 by an OpenAI reasoning model with leading mathematicians, then verifying, simplifying, and extending the AI-generated proof. It is a striking case of human-machine collaboration: the machine found the counterexample that had eluded mathematicians for eighty years, and human experts turned it into digestible, verified mathematics. The example was chosen deliberately: AI is beginning to contribute to advanced mathematical reasoning in ways that challenge long-held assumptions about human-only insight. Knowledge production itself is changing, and so is learning.

From there, he turned to the classroom. AI should not simply be added to education; it should help redesign the learning process through human-machine collaboration. Rather than rote memorization and repetition, learning should shift toward higher-order thinking, competence development, and stronger links between school, society, and industry. His most practical observation was that AI can finally enhance mass education with personalization — by tracking learner progress and adapting content in real time, it can support a shift from teaching students how to solve problems, toward helping them learn how to think.

The two talks describe the same transition from different angles. Prof. Tan's "engineering intelligence" and Prof. Ng's "human-machine collaboration" both reject the idea of AI as mere add-on. The future of work needs to be systemic: rebuilding how learning is delivered, measured, and connected to the world outside the school gate. That is precisely the belief behind our "AI&I" initiative and courses at ConnectAR — AI literacy first, then hands-on building, so that students step into this era as creators rather than mere users. And as a result, our future generation can work alongside AI, and thrive with AI... instead of being threatened by AI.

Events like this have been HKAAST's raison d'être for the past 40+ years. Helping to bring young scientists, veteran engineers, and educators into the same conversation is exactly the kind of connecting — of knowledge, people, systems, and purpose — that both speakers called for. I left the Dialogue grateful to have been part of it. Congratulations on the wonderful work of all the organizations and friends involved.

P.S. While I was attending the Dialogue, my kid was at his school watching a robotics competition. For younger students in K-12, I feel that motivation is also an important part of education, especially in the age of AI. Maybe get interested means just taking a small step of watching others first. The path to becoming a creator starts with cultivating interest, and even excitement. We have observed similar excitement in our courses, and will do our best to achieve that here at ConnectAR.

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