Khan Academy chief learning officer Kristin DiCerbo spoke with The 74 on the promise and limits of artificial intelligence in schools. This interview has been edited for length and clarity.
What are the latest developments in improving Khanmigo?
We definitely see some interesting things we didn’t necessarily expect. Students who are English language learners really like and use the supports in other languages. It’s important to have instruction on how to use new technology and tools. For students, how do you ask good questions? And for teachers, how do you integrate it?
The other thing is that we have found that Khanmigo as a tutor works best when it is paired with educational content we have already created. It is better integrated and has lower error rates when it’s using, and has reference to, the existing problems that were written and verified by people — and not just the problems, but the [step-by-step] hints and the answers that already exist in our system.
We see Khanmigo as a tutor, but the teacher is fundamental to this whole process.
How do you feel about AI tutoring generally and Khanmigo specifically?
I don’t think this is The Golden Ticket that’s going to save us all and be the sole reason that educational outcomes improve. I do think it still can be an important tool in the toolbox.
There is a lot of noise about what may or may not happen. We are basically sticking to “What are our technology partners doing, and what are we able to then partner with them to build?” And we will see what actually comes to fruition and deal with it if and when anything actually happens. We’re not counting on anything either way.
The earthquake that happened with the Chinese AI startup Deep Seek has been interpreted as causing supreme havoc at places like Open AI. Has any of this rebounded to you guys?
It’s part of what we have thought is likely to be the future. The models themselves become a commodity. Even since we launched, the prices have come down so far that it’s significant. We’re able to offer what we do at significantly lower prices, and that’s just likely to continue. And it’s not going to be the models themselves that are the “moat” or the differentiator — it’s going to be what people build with them.
Deep Seek’s model is open-source, so you can install it on your own machine. And that’s part of the concern about security and privacy with the app that, of course, has ties to the Chinese government. Then there’s the question about the model itself, as an open-source model. How does it perform? I would not rule out us using open-source models from different sources, but they would have to be evaluated, like all our models are, for security and privacy and their performance.
AI can make assessments better: more invisible, more customizable, and help teachers adapt instruction. What are you seeing in terms of the ways AI is moving into that field?
The assessment conversation has lagged a bit behind the learning conversation when it comes to AI. Traditionally we’ve had multiple-choice tests. There’s the whole game-based, simulation-based movement.
What does AI let us do? The idea of a conversational-based assessment is interesting. What if the assessment looks like what happens when a teacher sits down next to a student and says, “Explain your thinking. How did you get to that?” There’s a conversation there. That could potentially be an interesting way of adding to assessments that we already have. Of course, there would be questions: Is that standardized? Because different kids might get different questions as they engage in this conversation. How do we deal with that when we’re talking about high-stakes assessment?
Helping teachers and parents make sense of assessment data and get recommendations is interesting. Can AI help with that? Instead of getting this printout that says, “Your student got a 580 on this,” and you’re like, “What does that even mean? What should I do?” If you could have a conversation about that, that might be an interesting piece. We’ve been exploring that in something we have called Class Snapshots and recommendations that allow teachers to talk about their students’ Khan Academy performance. What else might they assign? How might they group students based on those kinds of things?
I’ve been playing around with AI tools that summarize and analyze big chunks of text and YouTube videos and whatnot. It strikes me that we are going to become so used to having a tool like this break things down for us that if schools can’t help us break our students’ performance down, we’re going to be disappointed.
I know lots of people that similarly are really getting into the habit of whenever they get a large amount of information, put it into an AI tool and get the summarization. I’m not quite sure how broad-based that is when we think about all the parents out there and all the schools, but that is what I’m seeing, and it might become an expectation in the near future.
Is there something on the horizon that you are looking at that maybe others aren’t paying attention to — good, bad or other?
We’re starting to get to a place where the AI is seeing what the student is working on and is able to interact with that and move forward. I’m pretty excited about that.
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