How AI could transform the way we measure kids’ intelligence
There is a saying in education that you treasure what you measure. Going by the standardized tests that dominate schools in many countries around the world, we’re teaching children that we value only a very narrow definition of intelligence—the ability to solve word problems about train times, or identify the purpose of a World War I treaty on a multiple-choice test.
The truth is human intelligence is vast and complex. Yet it is measured—and valued—crassly. And in an age when artificial intelligence is capable of nailing IQ tests and mastering knowledge-based curricula, humans may be setting ourselves up to be outshone by technology.
“I think we are in danger of dumbing ourselves down,” says Rose Luckin, a professor of learning-centered design at University College London who has been studying artificial intelligence and learning for more than 25 years. Because we measure intelligence in very limited ways, “we are very impressed by the sort of intelligent behavior our technology can produce.”
Luckin’s latest book,Machine Learning and Human Intelligence: The future of education for the 21st Century, argues that if we want to avoid turning our kids—and their teachers—into robots, we have to radically redefine intelligence. She advocates using AI to help us develop and measure human intelligence in various forms to better prepare students for a workplace that requires constant adaptation and learning.
Luckin identifies seven kinds of intelligence that kids will need to thrive in the future.
First, there’s interdisciplinary academic intelligence, the ability to tie subjects together rather than studying them in silos. (Finland, of course,is ahead of the curve on this front, having jettisoned the idea of teaching by subjects in favor of showing students to make connections between math, history, economics, and language under umbrella topics like “The European Union.”)
Then there’s social intelligence, or developing an awareness of our own emotions and how we regulate those in a group. This is something humans can excel at; robots, not so much.
Luckin also says that there are four meta-intelligences:
Meta-knowing, or our relationship to knowledge. Do students “understand where knowledge comes from?” Luckin asks. “Do they see it as something they are given and they have to learn, or do they realize it is something they construct and is contextual?” Kids with this kind of intelligence understand what constitutes good evidence, and how to make judgments based on that evidence.
Metacognition, or knowing ourselves and regulating our cognitive processes. (For example, if know I’m a procrastinator and someone who needs to write things down to learn them, I should not wait until one hour before a major exam to try and re-write all my notes.)
Meta subjective intelligence, or understanding our emotions and their relationship to our learning and well-being. Motivation is a key piece of this.
Meta contextual intelligence, which is about the dynamic context in which learning takes place—not just in a class, but with people, things, and locations. “Our intelligence is not just in our brain,” Luckin says. “There’s an increasing amount of evidence that context is huge,” and context is something AI can’t do well, Luckin says.
Accurate perceived self-efficacy, our ability to assess our own abilities, is perhaps the most important kind of intelligence. “Can we accurately predict whether we are likely to be successful at something, whether we are effective?” Luckin asks.
Humans are notoriously bad at predicting our own performance. In general, behavioral psychologists and economists have shown we are prone to overconfidence, among other biases. Luckin argues this is where AI comes in.
“AI is a powerful tool to open up the ‘black box of learning’ by providing a deep, fine-grained understanding of when and how learning actually happens,” Luckin writes in Nature.
