The Machines Are Learning, and So Are the Students


Artificial intelligence is starting to take over repetitive tasks in classrooms, like grading, and is optimizing coursework and revolutionizing the preparation for college entrance exams.

Jennifer Turner’s algebra classes were once sleepy affairs, and a lot of her students struggled to stay awake. Today, they are active and engaged, thanks to new technologies, including an artificial intelligence-powered program that is helping her teach.

She uses the platform Bakpax that can read students’ handwriting and auto-grade schoolwork, and she assigns lectures for students to watch online while they are at home. Using the platform has provided Mrs. Turner, 41, who teaches at the Gloucester County Christian School in Sewell, N.J., more flexibility in how she teaches, reserving class time for interactive exercises.

“The grades for homework have been much better this year because of Bakpax,” Mrs. Turner said. “Students are excited to be in my room, they’re telling me they love math, and those are things that I don’t normally hear.”

For years, people have tried to re-engineer learning with artificial intelligence, but it was not until the machine-learning revolution of the past seven years that real progress has been made. Slowly, algorithms are making their way into classrooms, taking over repetitive tasks like grading, optimizing coursework to fit individual student needs and revolutionizing the preparation for College Board exams like the SAT. A plethora of online courses and tutorials also have freed teachers from lecturing and allowed them to spend class time working on problem solving with students instead.

While that trend is helping people like Mrs. Turner teach, it has just begun. Researchers are using A.I. to understand how the brain learns and are applying it to systems that they hope will make it easier and more enjoyable for students to study. Machine-learning-powered systems not only track students’ progress, spot weaknesses and deliver content according to their needs, but will soon incorporate humanlike interfaces that students will be able to converse with as they would a teacher.

“Education, I think, is going to be the killer app for deep learning,” said Terrence Sejnowski, who runs the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies in La Jolla, Calif., and also is the president of the Neural Information Processing Systems Foundation, which each year puts on the largest machine-learning conference in the world.

It is well established that the best education is delivered one-to-one by an experienced educator. But that is expensive and labor-intensive, and cannot be applied at the scale required to educate large populations. A.I. helps solve that.

The first computer tutoring systems appeared in the 1960s, presenting material in short segments, asking students questions as they moved through the material and providing immediate feedback on answers. Because they were expensive and computers far from ubiquitous, they were largely confined to research institutes.

By the 1970s and 1980s systems began using rule-based artificial intelligence and cognitive theory. These systems led students through each step of a problem, giving hints from expert knowledge bases. But rule-based systems failed because they were not scalable — it was expensive and tedious to program extensive domain expertise.

Since then, most computer teaching systems have been based on decision trees, leading students through a preprogrammed learning path determined by their performance — if they get a question right, they are sent in one direction, and if they get the question wrong, they are sent in another. The system may look like it is adapting to the student, but it is actually just leading the student along a preset path.


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