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AI tutoring apps can support learning, but the evidence does not show that every app—or simply adding a chatbot—improves results. Two recent school trials found gains in specific, teacher-supported math programs. One also found that students often did not use the tutor when they got stuck. For families and schools, the key questions are what the app actually does, how it fits instruction, and whether students make measurable progress.
Are AI tutoring apps actually helping students learn?
There is promising but still limited evidence. Two evaluations published in 2026 reported improved math scores in particular school settings: one tested Khan Academy with Khanmigo in Tennessee middle schools, and another tested Gemini Guided Learning in junior secondary classrooms in Sierra Leone. These findings are meaningful, but they do not establish that all AI tutors work, that results carry over to other subjects or countries, or that an unsupervised chatbot produces the same gains.
Khanmigo in Tennessee: a two-year school trial
A two-year cluster randomized trial across 18 Tennessee middle schools examined Khan Academy with Khanmigo configured to coach students during existing remedial math sessions rather than simply supply answers. The National Bureau of Economic Research working paper reports an increase of 1.3 national percentile ranks per term, equivalent to about 0.06–0.08 standard deviations over a school year. The authors estimate a 0.14 standard-deviation gain for a full year of active participation. That estimate is not the average effect observed for every student: it reflects the authors’ estimate under sustained participation. Read the NBER paper.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →The trial also shows why access is not the same as learning. Although 96% of students tried Khanmigo at least once, the median student messaged it on only a third of practice days and in 17% of exercise sessions in which they made a mistake. These are figures from this trial, not general adoption rates. The authors say the gains resembled those from Khan Academy practice without AI assistance and identify student engagement as a binding constraint.
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Gemini Guided Learning in Sierra Leone: an eight-week trial
Google DeepMind reports that its pre-registered, eight-week evaluation involved 1,763 junior secondary students in 12 schools in Port Loko District, Sierra Leone. Students using Guided Learning scored 0.258 standard deviations higher in math than the control group. The program was used alongside teacher-led lessons and was conducted with Fab AI and Sierra Leone’s education ministry; it was not a test of general-purpose Gemini use in independent study.
Google DeepMind also reports that students benefited more in classrooms where teachers integrated Gemini into about half their lessons, toward a 12-hour target. Its analysis found that students who entered with stronger performance benefited most, which raises an important equity question: an average gain does not mean every learner benefited equally. These are company-reported results from one district and one program, not proof of comparable effects across other education systems or products. Read Google DeepMind’s account of the trial.
How to read the two results
The effect sizes should not be treated as a head-to-head ranking. The trials involved different students, durations, instruction, products, and study settings; the Tennessee result is expressed per school year, while the Sierra Leone evaluation lasted eight weeks. Both are evidence about structured school interventions, not a general answer about every app a student might download.
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The OECD’s January 2026 Digital Education Outlook described evidence on generative AI’s instructional effectiveness as emerging and mixed. Its chapter said no published Khanmigo effectiveness studies were available at the time it was written. That snapshot predates the NBER paper published in August 2026, so it should not be read as a current claim that no Khanmigo study exists. Read the OECD report.
What makes an AI tutor different from a chatbot?
A general chatbot can respond to a question, but a tutoring experience is intended to guide the learner through the work. It may ask what the student has tried, give a hint, break a problem into steps, or adapt an explanation. That design can make practice more interactive, but it does not guarantee accuracy or learning. The Tennessee trial is a reminder that even a coach-oriented setup needs students to engage with it.
Khan Academy describes Khanmigo as an AI tutor and teaching assistant built around guided discovery and its learning content. Its tools for educators include lesson planning, learning objectives, rubrics, and summaries of student work. The product page says family access for learners under 18 is parent-supervised, while classroom access is provided through school or district implementation. Eligibility and availability can vary, so check the current Khanmigo product page and FAQ for the arrangements that apply to your family or school.
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In an August 2026 announcement, Google said Khanmigo was using Gemini reasoning to create interactive diagrams for math and science. It also described a teacher-controlled practice tool: educators can provide materials, review and edit or reject generated questions, then approve an assignment. The announcement described early engagement signals, but did not report quantified learning outcomes for these new features. Read Google’s classroom-tools announcement.
Can an AI tutor replace a human tutor for your child?
The available studies do not establish that it can. The Tennessee trial tested a coached AI tool during school math sessions; the Sierra Leone trial included teacher-led lessons. Neither compared an AI tutor with a human tutor in a way that shows the app can take over a tutor’s role. A human can notice frustration, change pace, ask why a student chose an approach, and communicate with a family or teacher. An app may offer useful guided practice between those interactions, but a parent or educator still needs to judge whether its explanations are right and suited to the learner.
For younger children, students who need substantial individual support, or learners who struggle to spot incorrect explanations, adult supervision is especially important. For an older student working on a defined skill, an app can be a practical extra source of hints and practice. In either case, treat it as a learning aid rather than an authority or a substitute for instruction.
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How to choose an AI tutoring app
There is not enough comparable, independent evidence here to name a universal “best” app. Evaluate the fit for the student and the quality of the learning experience instead:
- Subject and age: Check whether it supports the subject and level your student needs, and whether family or school access is appropriate for their age.
- How it helps: Try a representative problem. Does it ask the student to reason, offer hints, and correct misunderstandings, or does it jump straight to a finished answer?
- Curriculum fit: Look for alignment with the student’s course materials or a clear content library. A plausible explanation can still conflict with a class method or assignment.
- Adult controls: For family use, establish how a parent supervises access. For school use, check what teachers can review, edit, assign, and monitor.
- Practical access: Confirm language support, accessibility features, device and internet requirements, geographic availability, and current price directly with the provider.
- Evidence quality: Ask who was studied, for how long, in what instructional setting, and what outcome was measured. A school trial with teacher support is not evidence that independent use will produce the same result.
How families and teachers can tell whether it is working
Judge an AI tutor by demonstrated skill, not by time spent in the app or an impressive conversation. Before a trial period, identify a specific learning goal—such as solving a type of equation or explaining a science concept—and use ordinary classwork or a short check of understanding to establish what the student can already do.
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- Set a bounded goal. Choose one skill that matches current classwork rather than asking the app to improve everything at once.
- Watch the learning process. Notice whether the student attempts the problem, uses hints, and can explain the reasoning instead of copying a generated solution.
- Check the explanation. Compare uncertain answers with a textbook, class notes, teacher guidance, or another trusted course source.
- Look for transfer. After practice, ask the student to solve a fresh problem without the app and explain the steps. Improvement on a new task is more useful than successful completion with prompts.
- Adjust or stop if needed. If the tool repeatedly gives unsupported answers, confuses the student, or does not improve independent work, change the settings or practice method, or return to a teacher or human tutor.
Teachers can apply the same standard to classroom tools: use them where they support a defined lesson objective, retain control over assigned material, and check student understanding independently. Google’s description of the Khanmigo practice tool, for example, gives teachers a review-and-approval step for generated questions; that control is useful, but the announcement does not establish learning gains from the feature.
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What the evidence means for schools and families
AI tutors can make hints and explanations available during practice and may help teachers prepare or adapt materials. The 2026 trials suggest that carefully designed, supported use can improve math outcomes in particular settings. They also leave important questions open: whether gains last, which students benefit most, how much adult support is needed, and whether results transfer to other subjects, products, and regions.
Conrad Sackey, Sierra Leone’s minister of basic and senior secondary education, described the trial as evidence that carefully designed AI can support teachers and improve learning outcomes. That is the minister’s view accompanying the announcement; it is not a substitute for examining the trial’s methods and limits. The practical standard remains straightforward: use the tool to support instruction, supervise it appropriately, and look for progress in what the student can do without it.
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