Skip to Content

Wire All Schools? Not So Fast…

The jury is still out on how valuable computers are for education–so let’s not succumb to political fashion and rush to wire all our students.
September 1, 1998

A few months ago, Israeli Prime Minister Binyamin Netanyahu explained to a group of politicians and computer professionals how he wanted to provide a quarter-million of his country’s toddlers with interconnected computers. Netanyahu was concerned because he had had trouble funding the project. I turned the tables and asked him why he wanted to do so in the first place. He was stunned, since it should have been obvious to anyone-especially to an MIT computer technologist-that computers are good for learning.

Throughout the world, droves of politicians, led by those in the United States, are repeating this fashionable mantra as they proclaim that millions of children in thousands of schools will soon be interconnected. You can feel their rush: “Isn’t it so responsible and oh-so-modern to put an emerging technology to work toward the noblest of social goals: the education of our children?” Not quite.

After 35 years of experimenting with computers in various aspects of learning, the jury is still out with respect to the central question: “Are computers truly effective in learning?” That’s what most educators who experiment with computers disclose, as I have steadily heard them say, for example in the Technology-in-Education Conference held in May in New York.

Certainly the promises of computers for learning are impressive. Simulation, for example, is already a proven winner. Besides pilots, tank commanders in the Gulf War who spent a great deal of training time on tank simulators attest to the success of this approach. Simulation can be nicely extended to other kinetic and quantitative tasks such as learning how to drive, ski, swim and sail, and, someday, even perform surgical operations. But we may be unable to build simulators for more qualitative situations, such as teaching a manager to handle a disgruntled employee, through a video simulation of the encounter.

Stand-alone computers can also help people write, compose music, generate designs and create new artifacts by bringing to our fingertips approaches, techniques, forms and patterns that have been successful in similar past endeavors. Machines are particularly effective as literacy tutors for adults who don’t have to feel embarrassed as they read aloud from a primer to a machine that listens to them and corrects their mistakes. Computers can also be used as “tutors,” for example by students who learn French by interacting in French with an adventure game in which the goal is to rent an apartment in Paris. The bolder notion of computer apprenticeship, where a Frank Lloyd Wright simulator analyzes your architectural drawings as the great master might have done, is still in the imagination stage.

Moving to the world of interconnected computers, we can see them being used for straightforward tasks such as sharing teacher information, posting homework on the Web (thereby eliminating children’s ancient excuse that they forgot their homework assignment) and getting useful information from trusted sources, as well as for many educational activities that involve e-mail and access to distant Web pages. Distance learning is another emerging capability of interconnected computers, particularly useful in matching locales where certain teaching specialties are unavailable with places that have the right people and the right knowledge.

If we can agree on some shared conventions, we might even construct my dream-a distributed virtual world heritage museum where each nation posts its writings, sculpture, music and other cultural offerings on the Web and the rest of us, flying a virtual histori-copter, soar easily in space and time, from Plato to Confucius to the Renaissance.

The achievements and promises of computers for learning go to the heart of the information revolution: Unlike the agrarian and industrial revolutions that helped learners indirectly by feeding them, transporting them to school and providing them with electricity, the information revolution helps much more directly because it deals with the principal currency of knowledge- information.

In light of all this potential, how can anyone argue that the jury is still out? Well, take U.S. high school students. They consistently rank from 12th to 18th, internationally, in physics and math, whereas Asian students rank first. Yet U.S. students have far greater access to computers than their Asian counterparts. Might there be some ancient, obvious and major thing about learning that we could learn…if we only lifted our heads long enough from our screens to look at our better educated neighbors? Another, possibly related, reason for the jury to be out is that learning may critically depend on what humans, rather than computers, do best: Lighting a fire in the student’s heart, role modeling and nurturing may contribute more to learning than the neatest hyper-linked courseware.

So what are we to do, confronted as we are by high promises on one hand and a jury that’s still out on the other? I suggest the same answer I gave to Prime Minister Netanyahu: Experiment creatively and massively (in the thousands), but refrain from deploying massively (in the millions)…at least until the jury has something to say. This won’t make politicians shine as bright, but our children may ultimately shine much brighter.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at with a list of newsletters you’d like to receive.