The turtle couldn’t do much of anything. It could move forward, backward, and turn. To get it to draw a square, you had to tell it how far to move, when to stop, how many degrees to turn, and how many times to repeat the operation. If the instructions were incorrect, the square simply wouldn’t appear.
In the late 1960s, when computers were still enormous and remote machines for the vast majority of the population, Seymour Papert was determined to make them accessible to children. He didn’t want to teach them computer science or prepare them for a future job market. He wanted to find out what happened when a child had to explain something to a machine that obeyed scrupulously but didn’t understand a thing.
The answer was Logo, the programming language developed starting in 1967 by a team that included Papert, Wallace Feurzeig, and other researchers. Its most famous character was that turtle: first a small robot that moved across the floor, and later a cursor on the screen.
Let’s imagine we want to have it draw a square. For us, “square” already contains a lot of information. We recognize the shape without needing to think about how it’s constructed. The turtle, on the other hand, doesn’t know what a square is. We have to explain it to it: move forward, turn 90 degrees, move forward again, turn again. And then discover that those instructions are repeated four times.
Suddenly, geometry is no longer just about recognizing a shape drawn in a book. You have to reconstruct it from the inside out.
Papert even encouraged children to get up and become turtles themselves. Walk around the room, stop, turn their bodies, calculate the angle. He called it playing turtle. The body served as a way to think through a mathematical problem and then translate it into the language the computer understood.
Papert had spent several years working with Jean Piaget in Geneva and was deeply influenced by Piaget’s idea that children construct knowledge by interacting with the world. He later moved to MIT and came into contact with another world: that of researchers trying to build intelligent machines.
From that encounter between Piaget and the early computers emerged an excellent question: What would happen if, instead of using machines to teach children, we let children teach the machines? Because to teach that turtle something, one had to understand it in a way that school rarely required.
The Right to Make Mistakes
Sometimes the turtle made a mistake. Or, rather, it did exactly what it had been told to do, which didn’t always match what the child wanted it to do. A turn of 80 degrees instead of 90 could ruin a square. A misplaced instruction would send the turtle in an unexpected direction. The drawing on the screen made it possible to retrace the path leading up to the error. You had to look, formulate a hypothesis, change a command, and try again.
In programming, that had a name: debugging. Papert saw in this practice something that went far beyond learning code. The error no longer served as the end point of an exercise. A program that doesn’t work is still an open problem: it forces you to ask what happened and what change might bring you closer to the desired result. In Mindstorms, Papert explicitly contrasted this logic with the school culture of “right” and “wrong” answers.
The difference is important. A number crossed out in red tells the student that something is wrong. A turtle that ends up facing the wrong way shows the consequences of their reasoning and allows them to intervene to correct them. Correcting requires understanding.
That is where constructionism began to take shape—the theory of learning that Papert would develop based on Piaget’s constructivism. Children construct knowledge, but that process takes on special power when they also construct something that exists outside their heads: a drawing, a program, a machine, a story—an object they can look at, show to others, discuss, take apart, and rebuild.
Mathematics was a particularly fertile ground for experimenting with this idea. Papert recalled that, as a child, he had been fascinated by gears and that those objects had helped him think about mathematical relationships. He dreamed that computers could become something similar: objects flexible enough for every child to find in them a gateway to ideas that might otherwise seem abstract.
Hence the title Mindstorms: Children, Computers, and Powerful Ideas. The “powerful ideas” were precisely those capable of changing the way a person thinks about a problem. But there was more. Programming required making thought explicit. To get the turtle to go somewhere, the child had to break down an intention into steps, anticipate what each step would produce, and explain each one precisely.
The machine didn’t think for them. It returned their own thoughts transformed into action.
While Papert was experimenting with Logo, other researchers were also envisioning the future of computers in the classroom. One possibility seemed obvious: using their capabilities to teach more effectively. The machine could present an explanation, ask a question, check the answer, and decide which exercise should come next. Teachers would have a more efficient tool at their disposal, and each student could progress at their own pace.
But Papert was deeply skeptical of that path. In Mindstorms, he articulated the difference through a reversal that would come to encapsulate much of his thinking: rather than a computer used to “program the child,” he proposed that it be the child who programs the computer.
It wasn’t a play on words. In the first model, technology refined an educational relationship that already existed. The computer could replace the worksheet with a screen, correct work more quickly, or adapt the exercises, but the student essentially remained in the same position: they were given problems designed by others and had to find the expected answers.
In the second model, the computer changed hands. The child could decide what they wanted to build and use the machine to achieve it. Perhaps a geometric figure. Or a game. Perhaps an animation. Knowledge then emerged in connection with the problems that arose during the project. One had to learn something because it was necessary to continue.
Papert did not dismiss teaching, nor did he imagine that children should discover all of humanity’s accumulated knowledge on their own. His criticism was directed at a school system organized around content divided into subjects, predetermined sequences, and answers that someone already knows.
In his view, the computer opened up the possibility of altering that organization. It also disrupted the traditional distribution of authority. A child might know more about Logo than his or her teacher. Two classmates might find completely different solutions to the same problem. The teacher was no longer necessarily the one who knew the path in advance but could become someone who helped students explore.
That is why Papert was much more than a pioneer of educational technology. He was interested in the computer because it could change who asked the questions, who made decisions, and who was allowed to experiment.
The Revolution That School Swallowed Up
Computers eventually arrived. The revolution, however, turned out to be much more elusive. During the 1980s, Logo spread with the advent of personal computers. That turtle, which had been born in research laboratories, appeared in schools across numerous countries. For a moment, it seemed possible that learning to program could become a way to transform children’s relationship with mathematics, science, and knowledge.
But something happened that Papert would observe with frustration: the school system had an extraordinary ability to absorb new technology without changing much. The computer could become just another subject. Logo could be taught as yet another set of instructions to memorize. And a machine conceived as a laboratory for experimentation could end up being used to do exercises, look up information, or reproduce on a screen activities very similar to those previously done on paper.
Papert came to believe that personal computers had changed society much more rapidly than schools had.
However, some of his ideas found other paths. In 1985, he began a long collaboration with LEGO. The combination of building blocks, motors, sensors, and programming made it possible to translate the logic of Logo into the physical world: build something, observe how it behaves, discover why it doesn’t do what’s expected, and modify it. The LEGO Mindstorms robotics line, launched in 1998, took its name precisely from Papert’s book.
Later, Scratch would carry on much of that legacy. Developed at the MIT Media Lab under the direction of Mitchel Resnick, a former student and collaborator of Papert’s, it replaced written instructions with blocks that could be combined to create stories, animations, and games. The goal remained the same: for children to use code to create and express themselves, not simply to learn programming. Educational robotics, maker labs, and much of contemporary creative learning culture also preserve something of that lineage.
But perhaps Papert’s greatest legacy is none of those things. It is a way of looking at technology. With every new machine that enters the classroom, we can ask what it does, how much it costs, what content it offers, or how much time it saves. Papert would propose an earlier question: what does it allow the child to do that they couldn’t do before? The difference remains enormous.
Now that machines already know how to think
The Logo turtle had a quality that seems almost exotic today: it was hopelessly dumb. It couldn’t guess what the child wanted. It didn’t fill in an incomplete instruction, didn’t suggest a better solution, didn’t write the program, and didn’t explain where the error was. To get anything out of it, you had to think.
Half a century later, we’re building exactly the opposite. A student can ask an artificial intelligence to solve an equation, write an essay, summarize a text, generate an image, program an app, or explain a concept they don’t understand. The machine interprets vague instructions, suggests approaches, and even corrects its own mistakes. Each new generation of models aims to require less effort to produce better results.
This reversal is fascinating when viewed through Papert’s lens. For decades, we wanted computers to understand people better. Now that they’re beginning to do so, an educational question arises that barely existed back when we were dealing with that turtle: how much thinking do we want the machine to do for us?
The easy answer is to frame the problem in terms of use: to allow artificial intelligence or ban it, to bring it into classrooms or keep it out. Papert’s work suggests a far more challenging question: What kind of intellectual relationship are we building with these machines?
AI can be used to avoid a problem or to make it more interesting. It can deliver a finished text or allow for a discussion of why that text doesn’t work well. It can also solve an exercise or generate models that the student must compare, deconstruct, and improve. It can reduce the student’s activity to simply making a request or become material to experiment with.
Papert died in 2016 and never got to see ChatGPT or the explosion of generative artificial intelligence. It would be risky to imagine what he might have thought of it. But there’s no need to do so to recognize the continued relevance of the question that ran through his work.
In the 1960s, when computers were inaccessible machines that took up entire rooms, Papert envisioned children playing with them. When they arrived in schools, he noted that having computers did not necessarily mean learning in a different way. And as the machines became increasingly powerful, he continued to argue that their value depended on what they allowed users to build.
Perhaps that is why that old turtle deserves a second look. It was slow, limited, and needed instructions for everything. It couldn’t write an essay or solve a problem on its own. But it forced a child to stop in front of a square and ask themselves how one is constructed.
Now that computers can answer almost any question, Papert poses a much more difficult one: what do we want children to still have to think about?
ProFuturo Observatory Team 
