In my previous post about MIT’s report on AI and education, I argued that universities should not merely protect traditional learning from AI. They should ask what students can now imagine and build that was previously impossible.
This question is also personal. As a student in Portugal, I encountered what I would call the adversarial layer of education: tests and examinations were often designed as confrontations between students and institutions. Difficulty itself was treated as evidence of rigour.
I survived that system partly because I had access to excellent books. Many equally capable colleagues did not and faltered. What appeared to be meritocracy was also, to some extent, unequal access to the resources needed to decode what education had failed to make clear.
The problem persists today. We continue to confuse opacity with rigour and failure with demanding education. In some subjects and examinations, very high failure rates coexist with an expanding dependence on private tutoring. When success requires families to purchase a parallel educational system, the examination is no longer measuring only what the school has taught.
When I later studied in the United States, I encountered something different. The demanding dimension of education had not disappeared, but it was more transparent. Students understood what they were expected to master and how they would be assessed. There were bridges between theoretical formulations and practical applications reducing abstraction.
Examinations consequently felt easier not necessarily because the intellectual standards were lower, but because difficulty was not created artificially through ambiguity.
At more advanced levels, that demanding layer acquired an entirely different purpose. It became a bridge to new ideas. Knowledge was not the destination; it was the structure that made discovery possible.
Artificial intelligence, simulation, software, sensors, robotics, 3D printing and printed electronics now allow us to take the next step. Students can move beyond understanding existing ideas. They can generate original hypotheses, model their consequences, fabricate prototypes and test them against reality.
There is also greater awareness today of what can happen after a prototype works: a research project may become a product, a company, a public intervention or even a new industry. In this respect, much of the world has learned from MIT and Stanford.
But one essential participant has remained comparatively absent: Nature.
Nature should not merely supply examples for science classes. It can become a source of strategies, materials, constraints, intelligence and surprise. It establishes the conditions within which Humans and Machines must operate.
Nature Inspirer, a proposed system through which a student could develop a nature-inspired product for the home of 2035, is presented in a concept site.
The Human defines the problem, purpose and values.
Nature provides strategies and establishes limits.
The Machine searches knowledge, connects distant ideas, generates alternatives and simulates consequences.
The student then fabricates a prototype and allows reality to answer.
Assessment must therefore ask more than “What knowledge did you acquire?”
What did you imagine?
What did you build?
Does it work in the real world?
What resistance did you encounter?
Who benefits—and who might be harmed?
Can you defend your decisions?
The prototype is not the answer. It is a hypothesis. Reality is the examiner.
None of these principles is new to my own practice. They have informed my teaching during four decades at university and now guide our work with younger students at Seixal Criativo.
What is new is the scale of what has become possible.
We can finally build an education in which Nature sets the limits, Humans define purpose and assume responsibility, and Machines enlarge imagination and foresight.
That, I believe, is the education of the future.