The Myth of Automated Learning
AI's real threat to education.
Among the general public, generative AI’s most enthusiastic early adopters have been students. Surveys conducted a year ago revealed that nearly 90 percent of college students and more than 50 percent of high-schoolers were regularly using chatbots for schoolwork. Those numbers are certainly higher now. AI may be the most rapidly adopted educational tool since the pencil.
Because text-generating bots like ChatGPT offer an easy way to cheat on papers and other assignments, students’ embrace of the technology has stirred uneasiness, and sometimes despair, among educators. Teachers and pupils now find themselves playing an algorithmic cat-and-mouse game, with no winners. But cheating is a symptom of a deeper, more insidious problem. The real threat AI poses to education isn’t that it encourages cheating. It’s that it discourages learning.
To understand why, it’s important to recognize that generative AI is an automation technology. You can speculate all you want about computers eventually attaining human-level intelligence or even “superintelligence,” but for the time being AI is doing something that has a long precedent in human affairs. Whether it’s engaged in research or summarization, writing words or creating charts, it is replacing human labor with machine labor.
Thanks to human-factors researchers and the mountain of evidence they’ve compiled on the consequences of automation for workers,1 we know that one of three things happens when people use a machine to automate a task they would otherwise have done themselves:
Their skill in the activity grows.
Their skill in the activity atrophies.
Their skill in the activity never develops.
Which scenario plays out hinges on the level of mastery a person brings to the job. If a worker has already mastered the activity being automated, the machine can become an aid to further skill development. It takes over a routine but time-consuming task, allowing the person to tackle and master harder challenges. In the hands of an experienced mathematician, for instance, a slide rule or a calculator becomes an intelligence amplifier.
If, however, the maintenance of the skill in question requires frequent practice — as is the case with most manual skills and many skills requiring a combination of manual and mental dexterity — then automation can threaten the talent of even a master practitioner. We see this in aviation. When skilled pilots become so dependent on autopilot systems that they rarely practice manual flying, they suffer what researchers term “skill fade.” They lose situational awareness, and their reactions slow. They get rusty.
Automation is most pernicious in the third scenario: when a machine takes command of a job before the person using the machine has gained any direct experience doing the work. Without experience, without practice, talent is stillborn. That was the story of the “deskilling” phenomenon of the early Industrial Revolution. Skilled craftsmen were replaced by unskilled machine operators. The work sped up, but the only skill the machine operators developed was the skill of operating the machine, which in most cases was hardly any skill at all. Take away the machine, and the work stops.
Because generative AI is a general-purpose technology that can be used to automate all sorts of tasks and jobs, we’re likely to see plenty of examples of each of the three skill scenarios in the years to come. But AI’s use by high-school and college students to complete written assignments, to ease or avoid the work of reading and writing, is a special case. It puts the process of deskilling at education’s core. To automate learning is to subvert learning.