The Promise We Thought We’d Have by Now
If you’d told me five years ago that artificial intelligence would tutor millions of students simultaneously, I would have pictured something like the learning equivalent of a 24/7 patient teacher who never gets tired, never runs out of explanations, and somehow remembers exactly where each student got stuck. It sounded almost too good to be true. By late 2025, Khan Academy’s Khanmigo had logged over 50 million tutoring sessions since launching in 2023, and the numbers looked genuinely promising. That’s not a trivial achievement. But here’s what I’ve learned after watching this technology develop and listening to colleagues who’ve integrated it into their classrooms: the real story is far more complicated than either the hype or the skepticism would suggest.

Khanmigo 2.0 represents a meaningful shift in how we’re thinking about personalized learning at scale. The improvements in math completion rates among middle school students, which jumped 23% for those using the platform, tell us something real is happening. But as educators who care about how learning actually works, we need to understand what’s working, why some students gain dramatically more than others, and where this technology is quietly undermining something precious about the learning process itself.

Where the Science Actually Backs It Up
Let me start with what genuinely impresses me about Khanmigo’s approach. The platform has built in a principle from learning science that I’ve been teaching about for years: the Socratic method actually works better than direct instruction when the goal is retention and transfer. Research from Stanford Graduate School of Education published in 2025 found something striking: AI tutors that ask guiding questions outperform those that simply provide answers by 31% on retention tests given two weeks later. This is not a trivial difference. This is the kind of gap that suggests students are developing deeper understanding rather than just following along while someone explains something to them.
What makes this particularly effective is that Khanmigo has built this into its architecture deliberately. Rather than defaulting to “here’s the answer,” the system is designed to prompt students to think through problems themselves. When I watch students interact with it, I see them slowing down, reconsidering their work, and actually explaining their thinking out loud. That’s the move we want. That’s where learning happens.
The gains for English language learners have been especially noteworthy. A 41% improvement in reading comprehension scores after eight weeks is substantial enough that it deserves real attention. For students who are simultaneously acquiring English and engaging with academic content, having a patient tool that can adjust its language complexity and provide instant feedback, without the social anxiety of asking a teacher or classmate for the hundredth time, makes real sense. The Gates Foundation clearly agreed, committing $15 million in Q3 2025 specifically to expand access in Title I schools across twelve states. That kind of investment suggests policymakers are seeing genuine promise in these results.
The Struggle Problem Nobody’s Talking About Enough
But here’s where I need to be direct about something that’s been bothering me since I first heard about it. A January 2026 survey by the RAND Corporation found that 67% of teachers using AI tutoring tools reported their students became less likely to engage in productive struggle. Let that sit for a moment. Two-thirds of teachers noticed their students backing away from hard problems more quickly.
This matters because struggle is not the enemy of learning. Struggle is often the learning itself. When a student sits with a confusing concept for a while, makes a mistake, notices it, revises their thinking, and arrives at understanding through that process, something happens neurologically that’s different from being guided smoothly to the answer. The neural pathways that form through struggle tend to be stickier. More retrievable later. More transferable to new problems. We know this from cognitive science.
What I suspect is happening with some implementations of Khanmigo is subtle but important: students are learning that help is always just a click away. When the friction of not knowing something is reduced too far, when the tool makes it easier to get an answer than to keep thinking, students rationally decide to opt out of the cognitive work. I see this in my own classroom when I’m not intentional about when I deploy tools and when I deliberately hold space for productive confusion. The tool itself isn’t the culprit. The way we’re using it sometimes is.
What 2.0 Gets Right That Earlier Versions Missed
Khanmigo 2.0 has started addressing some of these concerns, at least in its design if not always in how schools implement it. The platform now includes better controls for educators to set parameters around how quickly hints are offered. You can configure it so students have to genuinely think before the tool jumps in. That’s the kind of design decision that shows the team understands the learning science here.
The efficacy data from Khan Academy Khanmigo Research and Efficacy also reveals something encouraging: the biggest gains aren’t appearing uniformly across all students. They’re concentrated where you’d predict the tool would be most useful. Students who previously had no access to individualized tutoring support are seeing the largest improvements. That’s exactly right. This technology fills a gap. For students in under-resourced schools, for students whose parents can’t afford tutoring, for students who struggle with asking for help in front of peers, Khanmigo is genuinely democratizing access to something valuable.
The Implementation Problem We Can’t Ignore
Here’s what concerns me most, though: Khanmigo itself might be excellent, but how schools are implementing it often isn’t. I’ve watched administrators roll out AI tutoring tools as a replacement for human connection rather than a supplement to it. I’ve seen it positioned as a cost-saving measure when budget cuts hit. I’ve heard about class sizes staying enormous because “now they have Khanmigo” to help students. That’s a betrayal of what the tool could be.
The research from Stanford PACE Center AI in Education Reports consistently shows that AI tutoring performs best when it’s part of a comprehensive learning environment, not a substitute for one. The students seeing 23% improvements in math completion rates, the ELL students seeing 41% gains in reading comprehension, I’d bet money that many of them are in settings where the AI tool is one resource among many, where they also have human teachers who care, who notice when they’re struggling productively versus actually stuck, who remember their names.
The question we should all be asking as educators, parents, and policymakers is this: Are we using Khanmigo to enhance human teaching or to replace it? The technology gets it right in its architecture and design. But we get it wrong sometimes in our choices about deployment. The tool isn’t the whole story. Our wisdom about how to use it is.
What This Means for Your Learning (Or Teaching)
If you’re a student considering using Khanmigo, here’s my honest take: it’s genuinely useful for targeted help on specific concepts, for getting unstuck when you’re truly stuck, for practicing once you already understand the basics. But don’t let it become a shortcut around thinking hard. The sessions that will actually change your brain are the ones where you struggle first, ask questions second, and understand why you were confused third.
If you’re an educator thinking about implementing this, the research is real and the potential is genuine. Use it intentionally. Set up your systems so students experience productive struggle before accessing hints. Use the time you save from grading routine practice problems to do what only humans can do: notice the specific confusion underneath a wrong answer, connect concepts to a student’s particular interests, celebrate the exact moment when understanding clicks. Build your classroom so the AI tool serves your teaching, not the other way around.
What’s your experience been with AI tutoring tools? Whether you’re using Khanmigo, considering it, or watching from the sidelines, I’d genuinely love to hear what you’re seeing work and what concerns you. The conversation about how we implement these tools matters as much as the tools themselves. Drop a comment below or reach out, I read every single one.













