How AI could enable us to leave the ‘factory model’ of education behind

How AI could enable us to leave the 'factory model' of education behind

Today’s educational systems, in general, continue to function today as they did in the 19th century , that is, according to the "factory model": all students learn at the same speed, under the same procedures, in the same place and simultaneously (in more or less arbitrary biological age segments).

But this "assembly line" approach in which a few educators must manage a large number of learners and, through a clinical eye based primarily on the subjective or on passing tests that essentially demonstrate that they have memorized a syllabus, could remain finally relegated to oblivion thanks to various artificial intelligence techniques .

This educational model, perpetuated for decades, was difficult to overcome until now given the limitations of teaching resources (time, attention, supervision), but now we are about to access a scenario where these resources will have a marginal cost close to zero: visual identification, voice recognition, creation of a detailed profile based on past behavior, etc.

In other words, it will not matter whether there are ten students or ten thousand in a class, the aforementioned techniques can also provide a highly personalized educational experience. A more faithful approach to what it really means to learn, embrace wisdom, sharpen a critical sense, rather than an accumulation of stagnant data, isolated from each other, and barely managed by an ideologically and psychologically biased teacher with a dwindling time available .

Empowered education

Thus, artificial intelligence can improve four educational scenarios to varying degrees : classroom teaching, homework and exercises, exams and grades, and personalized tutorials. In countries like China, in fact, education in these four dimensions is already being addressed thanks to algorithms, especially thanks to its Squirrel centers.

Thanks to artificial intelligence, a large amount of big data can be collected, not only from the individual student, but from all students and their various psychological profiles, in order to optimize teaching in the future. As Kai Fu Lee explains in his book Superpowers of artificial intelligence :

That profile contains a detailed account of everything that affects a student’s learning process, such as the concepts they already understand well, those they do not yet, how they react to different teaching methods, how attentive they are in class, how fast who answers the questions, and their incentives.

Facial recognition will be used for roll call, but also to check the degree of attention of the students and evaluate the level of understanding based on gestures such as nodding or shaking the head or expressions of perplexity .

Upon arrival home, the student’s profile will be combined with question-generating algorithms to create homework configured exactly according to the student’s abilities and shortcomings. In this way, we will allow the most prepared or advanced students to go further; also that those neuropsychologically better adapted for some disciplines compared to others direct their steps towards that path.

Released all this time for human teachers, they will be able to spend more time with each student, especially focusing on those where the algorithms do not reach. Even the most backward profiles can be notified to parents, providing a clear and detailed idea of ​​concepts that your child is not quite familiar with. Because education is also something that should be encouraged at home .

Almost all of the tools described here already exist, and many are already being implemented in different classes throughout China. Taken together, they constitute a new AI-driven paradigm in education, a model that merges the online and offline worlds to create an educational experience tailored to the needs and capabilities of each student.