At the recent WSIS Forum 2026 in Geneva, the IFIP IP3 Global Industry Council (GIC) organised a workshop on The Workforce of the Future: Workplace Transition and the Role of Education, exploring AI’s impact on the future workforce and the implications for education. GIC Director Eliezer Manor introduced attendees to the transformation of human curiosity into structured innovation through regenerative AI using Curiosity Engineering, a method he recently applied in a live MBA workshop at Azrieli College in Jerusalem. You can read his contribution below.
Dear colleagues, friends, members of the IFIP IP3 community and GIC members,
Thank you very much for the opportunity to speak with you today.
I would like to share with you a concept, a discipline, and a practical technique that I have recently been developing and applying successfully in a live MBA workshop in Israel. The name of this new domain is Curiosity Engineering.
Curiosity Engineering is based on a simple but powerful idea:
AI Generates Answers. Humans Generate Meaning.
Artificial Intelligence is developing exponentially. Its capabilities are astonishing. But by itself, AI does not care. It does not feel urgency, fascination, responsibility, hope, or meaning. These remain human qualities.
And this is exactly why I believe the future does not belong to human beings alone, and certainly not to independent AI alone. AI without human direction may become a threat. The real opportunity is something else:
It is Human/AI hybridization.
Not people alone.
Not AI alone.
But a continuous, interactive, iterative process in which human curiosity, imagination, intention, and judgment work together with the immense generative power of AI.
In my view, this hybridization is one of the foundations of the next Industrial Revolution.
And in the field I am working on now, this hybridization is dedicated to ideation for hi-tech innovation.
This is where Curiosity Engineering enters.
Curiosity Engineering is a new domain with its own discipline and techniques. It is the structured process through which human curiosity activates AI and guides it through iterative exploration toward innovation.
At the practical level, this process is carried out through what I call: Regenerative AI.
Traditional prompting is often one-shot: you ask, AI answers, and the interaction ends.
But Regenerative AI begins where ordinary prompting ends.
It is like a ping-pong game between human curiosity and imagination on one side, and Generative AI on the other side. The ball goes back and forth. A human asks. AI answers. The answer stimulates a new question. That question produces a new answer. And so on, until the human gets the hint for a new possible product, service or application.
Sometimes many “balls” remain in the exploratory space — many promising directions, many divergent questions. And sometimes, after several exchanges, one ball crosses the net in a decisive way and becomes a concrete innovative idea.
That is why this metaphor of ping-pong is so useful: it captures the dynamic, iterative, and regenerative nature of a convergent process.
The technique itself begins with what I call a Dedicated Question.
A Dedicated Question is the initial question formulated by the human. It does not come from AI. It comes from curiosity, need, wonder, frustration, observation, or aspiration.
In fact, in my workshop I present 17 different ways to stimulate the formulation of a Dedicated Question. This is important, because innovation does not begin with answers. It begins with the ability to formulate a meaningful starting question.
Then comes the second and most important operational element: the Derivative Question.
A Derivative Question is a new question that uses a word, term, or concept from the previous AI answer. This is the rule of the technique. The next question must grow out of the previous answer.
Let me demonstrate very briefly.
Suppose the Dedicated Question is:
“How can we prevent drowning before a person loses consciousness?”
A short, summarized AI answer might be:
AI may answer that drowning could be prevented by using wearable sensors that detect early physiological distress and trigger an automatic action.
Now comes the first Derivative Question.
It must use a term from that answer. For example, the term “physiological distress.”
So, the first Derivative Question could be:
“Which indicators of physiological distress can be monitored reliably in real time in water?”
A short, summarized AI answer might be:
AI may answer that relevant indicators include heart rate, blood oxygen level, abnormal motion, breathing irregularity, and panic-related movement patterns.
Now comes the second Derivative Question.
Again, it must use a term from the previous answer. For example, the term “blood oxygen level” or “heart rate.”
So, the second Derivative Question could be:
“How can heart rate and blood oxygen level sensors be integrated into a practical wearable life-saving device?”
And already, after only two regenerative loops, we are no longer speaking only about drowning in general. We are beginning to move toward a concrete innovation concept of an automatic inflatable life jacket.
This is the essence of the method.
A good answer is useful. A good next question is regenerative.
During the recent weeks, I applied this approach with MBA students at Azrieli College in Jerusalem, in a workshop on AI-assisted ideation for hi-tech innovation. The results have been very encouraging. The students are not only receiving answers from AI. They are learning how to think with AI, how to guide it, how to deepen exploration, and how to transform curiosity into innovation.
This, I believe, has broad implications.
It can be applied anywhere in the world. It is not limited by local culture or educational tradition. Young people everywhere are curious. What they need is a disciplined way to turn curiosity into structured exploration: generating a Dedicated Question, followed by systematic Derivative Questions.
And this is why I believe Curiosity Engineering is relevant not only to innovation, but also to professionalism in general and ICT in particular, for education, and for specific goals discussed for example in the WSIS and SDG context.
Responsible AI Begins with Responsible Questions.
If we want AI to serve humanity, we must not only improve the machines. We must also improve the human disciplines that guide them.
Curiosity Engineering is one such discipline.
It welcomes the exponential growth of AI, but insists on human direction, human meaning, and human responsibility. AI is the force multiplier. But the orientation must remain strict human.
So, my message today is simple:
Without curiosity, AI is idle.
AI generates answers. Humans generate meaning.
A good answer is useful. A good next question is regenerative.
Responsible AI begins with responsible questions.
And therefore:
The future ICT professional must become a designer of questions, rather than only a programmer.
Machines can compute.
AI can generate.
But only humans can care.
And caring is the origin of meaningful curiosity.
Let me finish with a short summary.
Curiosity Engineering is a new discipline I am developing to transform human curiosity into structured innovation through AI. Its practical method, Regenerative AI, is based on an iterative human–AI dialogue in which every AI answer becomes the source of the next human Derivative Question. The process begins with a human Dedicated Question, driven by curiosity, imagination, need, or aspiration, and then develops through repeated questioning loops toward concrete hi-tech innovation concepts.
At the heart of this process are Guided Curiosity and Guided Imagination: the human ability to direct curiosity, shape questions, recognize meaning, and guide AI toward a valuable purpose. I recently applied this method successfully in a live MBA workshop at Azrieli College in Jerusalem, where students learned not only to use AI, but to think with AI. The deeper message is that the next Industrial Revolution will not be driven by humans alone or by independent AI alone, but by human-driven AI hybridization. In this hybrid process, AI becomes a powerful force multiplier, while human meaning, responsibility, curiosity, and imagination remain in control.
In short: AI generates answers, but humans generate meaning — and the future ICT professional must become a designer of questions, not only a programmer.
Thank you very much.
The IFIP IP3 Global Industry Council (GIC) serves as the principal forum for employers and educators to engage with IP3 and shape the global ICT profession. Each month, they feature relevant and insightful ideas in IFIP Insights.
Image: GIC director Eliezer Manor speaks about Curiosity Engineering, the structured process through which human curiosity activates AI and guides it through iterative exploration toward innovation.