There's a whole wing of the edu-wonk world pre-occupied with "high-impact tutoring," whatever that is presumed to be. The field serves as a successor to the notion of "accelerated learning," the idea that learning gaps could be closed by teaching the students who were behind by teaching them more, faster, than the other students-- an idea that stunk for a variety of reasons.
"High-impact tutoring" is also a successor for "two-sigma tutoring," another idea that rested on a single very small, narrow, and largely debunked study.
What all of these ideas have in common is that they are super-appealing to techbros anxious to sell some version of AI tutoring. Lauraine Langreo over at EdWeek took a look at some studies about the various ways AI can be deployed for tutoring, and she opens with what has been the guiding principle for all of these operations.
High-impact tutoring has been heralded as an evidenced-backed solution for addressing the significant academic gaps that schools are facing. But scaling and sustaining these programs are challenging for schools because they require a lot of human tutors, time, and money.
"Has been heralded" is doing a lot of work here, but the idea that it takes a lot of humans spending a lot of time (and wanting to be paid for it) is spot on. That crucial fact about tutoring smells a lot like money to AI companies. "However," as she notes, "research on the effectiveness of AI tutoring models is limited and still emerging."
Well, that's kind. Sal Khan has taken a good hard look at his own products and discovered that-- surprise--when you plunk a young human in front of a screen and say, "Go ahead and use this program to teach yourself math," by far the most common response is "I wonder if I can get regular Youtube on this thing." Students simply don't use it.
Langreo is looking at a "brief" from Stanford that considers how AI might be used.
SCALE, part of Stanford's Accelerator for Learning (yes, that again), has a brief out that looks at the ways AI can be used, and well...tutoring by a human has a "robust evidence base." After that, every evidence base is "emergent," a research term that means roughly "there is no actual evidence of this in sight, but we kind of smell the possible concept of a piece of the beginning of a sprig of evidence just about to appear, but it's not here yet."
SCALE likes a human tutor with AI supporting the tutor behind the scenes, which they call "potentially as effective, or more effective" as an actual unaided human. This would, of course, depend on the human. But if Human plus AI gets you the same results as Human alone, that suggests at the very least that the extra cost of AI is a waste of money. If the problem your leaders are trying to solve is "How do we incorporate AI into our program," maybe this looks not so bad, but if the problem you're trying to solve is "How do we best tutor these young humans," then adding AI seems like a non-starter (even before we start considering, as SCALE does not, the various negatives associated with AI.
"AI tutoring with human support," which has been the model of computer-powered schools, from Summit to Altschool to Alpha School-- the emergent evident here suggests that implementation matters, because it's only a shade removed from AI-only tutoring, which SCALE declares has "unknown effectiveness." Sad news for everyone who thought they could just swap out an actual human tutor for some AI program and get "high impact" results.
Look, we know the most effective means of tutoring-- one teacher, one or two young humans, repeated a few times a week. It's not a mystery. But there are two longstanding responses to this. One is the eternal education response from leaders and taxpayers: "That sounds nice and I can see it would make education better, but I don't want to pay for it." The other is corporations looking at it and thinking, "I bet we could make money pretending to fix this problem."
But if you want to get some actual tutoring done, you are going to have to hire a knowledgeable human being to do it, not a bot.
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