If you knew everything, what incentive would you have to find new things to learn? Probably none. Well, that is the reality of the traditional education system.

Most classes, including language classes, whose mirror is the traditional educational model base their methods on providing answers and content, rather than letting students find them by themselves. In other words, the desire to know and learn is discouraged.

In a world where information is widely available, it is pointless to continue basing training on the dosage of answers but to increase the ability to ask questions.

The more it is memorized and the more it is repeated, the less one learns and likewise the more the emotion of discovery vanishes.

The difference between evolution and a revolution is that if the answers changed in the former, the latter changed the questions.

In education, despite the enormous revolution that we live, people continue to give new answers (even some not so new ones) to the usual questions.

An unequivocal symptom of this would be the constant obsession with the professions of the future, although we are not very clear if in the future there will be professions because the concept of work and employment itself is questioned in an environment of artificial intelligence and growing automation.

This is probably so because it may be easier to sell more courses about the professions of tomorrow than to critically think whether there may be no professions tomorrow.

We continue trying to find out what are the necessary skills for jobs that do not yet exist and may never do so when the obvious is that the future will go from attitudes and not skills.

In (un) education, training in critical thinking is essential for the challenges that lie ahead which is not covered by the content provided by teachers, but with what the students discover on their own.

In a world with all available information, it is pointless to continue basing training on the dosage of answers but to increase the ability to ask questions.

Our current learning model may allow us to learn everything we know we don’t know, but it will leave in the dark that huge universe of things we don’t even know we don’t know.

We may be data scientists, poets, quantum physicists or philosophers, but above all we never lose that emotion of discovering.

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