What business models are working in AI in education?

Why is it suddenly harder to make money from AI in education?

AI in education is growing quickly, but charging for basic AI access is getting harder because students and teachers now get remarkably capable education features from general-purpose platforms for little or no money.

Google has pushed this pressure further with a dedicated Gemini student hub covering research, quizzes, flashcards and study organization, while eligible U.S. college students can get a year of Google AI Pro for free. OpenAI has lately expanded its own education push with a version of ChatGPT designed specifically for teenagers. Microsoft already gives eligible education users Copilot Chat and its Teach tools without requiring a separate AI subscription.

Chegg shows how brutal that shift can become for an established paid study product. In its latest quarterly results, total revenue fell 51% year over year to $51.8 million. Chegg has several problems beyond generative AI, and management has previously pointed to traffic losses from Google AI Overviews, so the whole decline cannot simply be blamed on ChatGPT. Still, paying for generic digital answers has become much less attractive when answers are everywhere.

The companies with something more specific to sell are in a better position: curriculum, assessment, high-stakes outcomes, teacher workflow, institutional controls, implementation or existing distribution.

When will students actually pay for an AI tutor?

Students currently pay for AI tutoring when the product is tied to something concrete enough to justify the subscription, especially an exam, a language goal or another high-stakes learning objective.

Medly is becoming one of the clearest recent examples. The UK exam-prep company has now passed 400,000 users after launching in 2025 and has just raised $8 million to expand. Its subscription costs £24.99 a month, while free users receive limited daily access.

The important part is what Medly chose to specialize in. It covers GCSE, A-Level, IB and other defined curricula, marks work against exam requirements and focuses on weaknesses that can cost students actual marks. According to the company, 74% of surveyed GCSE users improved their grades while using the platform. A more recent study cited by the company found a 22% performance improvement in a 900-student sample. The results are company-reported, so they deserve some caution, but they are still far more useful than engagement figures alone.

Medly also changed its pricing model this year. Instead of a short free trial, students can now use the product every day for free until they reach limits on interactive practice and AI feedback. Static learning material is cheap to distribute; repeated AI marking and tutoring is where usage becomes expensive and where Medly asks heavy users to pay.

Duolingo gives us the mature version of the same idea. Its latest quarter ended with 12.7 million paid subscribers, up 17% year over year, while daily active users reached 58.7 million. Duolingo Max uses AI for premium conversational features, although Duolingo does not disclose how many of those 12.7 million subscribers specifically pay for Max.

Students appear much more willing to pay when AI sits inside a defined learning journey with an obvious goal.

Are generic AI tutor subscriptions already getting commoditized?

Generic AI tutoring is being commoditized very quickly, and every new education feature from the largest model companies makes the standalone position harder to defend.

Google's student-focused Gemini experience can generate quizzes, organize study material, explain photographed worksheets and conduct deeper research. OpenAI's teen product is explicitly designed to guide younger users through learning rather than simply hand over answers. Microsoft has already put lesson planning, quizzes, rubrics and adaptation tools inside Copilot for education users.

These products still leave plenty of room for specialists. The problem is that “AI that explains schoolwork” no longer qualifies as meaningful specialization.

Chegg's latest numbers offer a useful warning from an adjacent model. Revenue has now fallen by half year over year, and the company is deliberately shrinking and managing its academic-services operation for cash while trying to build a larger skilling business. Chegg's decline began before some of the newest education-AI products arrived, so there is no clean one-company causal story here. But its old advantage was convenient access to answers and explanations, exactly the capability general AI has made abundant.

The specialists that look healthier are adding constraints rather than more generality. Medly knows the exam specification. Khanmigo sits inside Khan Academy's curriculum and mastery system. Pearson grounds AI inside textbooks and courseware. School platforms can show a teacher what happened during a student's AI session.

Generic tutoring is sliding toward feature status. Companies can still build huge learning businesses around AI, but they need another reason for the learner to stay.

If you want more recent data on this point, please see our latest AI in education market report.

Is saving teachers time the best K-12 AI business right now?

Teacher productivity is currently the clearest K-12 AI use case because the value appears immediately and can be measured without waiting years for student outcomes.

Gallup and the Walton Family Foundation found that teachers using AI weekly saved an average of 5.9 hours per week. The common tasks were mundane but expensive in aggregate: lesson preparation, worksheets, assessments, adapting material and administrative work.

Brisk recently published a deeper study covering 542 certified teachers across four districts that had used the product for at least two school years. The study combined survey data with platform usage and received ESSA Tier III validation. Sixty percent of teachers reported meaningful time savings, while the share describing time management as easy or very easy rose from 34% to 60% after two years. Almost half of the teachers who saved time said they reinvested it in deeper instruction.

Those figures help explain why companies such as Brisk and MagicSchool spread so quickly among teachers. A teacher can create feedback, differentiate a text or draft a lesson today and know within minutes whether the software was useful.

The harder part is staying valuable once those basic generation features become common. Brisk has therefore moved deeper into student activities, analytics and Curriculum Intelligence, while MagicSchool is adding district-controlled student tools, LMS integrations and administrative controls.

Why would a school district pay for AI when teachers can use ChatGPT or Gemini for free?

School districts are paying for control, curriculum and visibility around AI, while free teacher adoption gives vendors a practical route into those contracts.

Chicago Public Schools has provided a good example of why access alone does not solve the institutional problem. As the new school year begins, the district has dropped a planned rollout of Google Gemini chatbots for high-school students amid continuing concerns around privacy and how AI should be used in classrooms.

District-focused platforms sell something different. MagicSchool's enterprise product gives administrators centralized usage data, district-approved AI tools, custom content grounded in local curriculum and policies, professional development and controls over student use. Its latest enterprise updates let district administrators build and distribute their own student-facing AI tools across selected schools.

Brisk has moved in the same direction. Its paid district tiers can incorporate a district's standards, rubrics, pacing and adopted curriculum. Administrators can see usage across schools and control which tools teachers and students receive. SchoolAI's paid plans include SIS rostering, LMS integration, administrative alert routing and session-level student insights.

The distribution model is increasingly clear as well. Brisk currently says more than two million educators use its product across more than 20,000 districts. Teachers can continue using a free plan indefinitely, while schools pay custom pricing for standards alignment, administrative visibility, student insights, professional development and curriculum integration.

SchoolAI has made an even more revealing decision. It recently removed the option for individual teachers to buy its paid product. Teachers get a limited free version, while Pro and Scale are now available only through school or district purchases.

MagicSchool continues to use the same broad funnel: free educator adoption first, then paid district deployment. Its current district partners include large systems such as Hillsborough County, Denver, Seattle and Northside ISD.

K-12 procurement remains slow, and free users do not automatically become enterprise revenue. But walking into a district with teachers already using the product is a much better starting point than cold-selling from scratch.

What free general AI can already do	What district AI vendors can still sell
Explain concepts and answer questions	District-approved learning environments
Generate lessons, quizzes and worksheets	Curriculum and standards alignment
Rewrite or translate material	SIS, SSO and LMS integration
Give students conversational help	Teacher visibility into student sessions
Provide general safety systems	District-specific rules and alerting
Help an individual teacher work faster	Usage analytics, training and implementation across hundreds of teachers

If you want more recent data on this point, please see our latest AI in education market report.

How much can schools realistically pay for AI?

Schools can support meaningful AI software businesses, but K-12 pricing remains low enough that vendors need broad deployments or several valuable workflows inside each contract.

Khan Academy gives us a transparent benchmark. Its Enterprise Starter package currently costs $10 per student per year for schools and districts buying up to 1,000 student seats. Teachers and administrators do not count toward that seat total.

At that price, 10,000 students represent only $100,000 a year before any negotiated discounts or additional services. That's not much in seat terms. A company therefore needs many districts, larger contracts or higher-value modules to build serious revenue.

This explains why the product roadmaps are broadening. Brisk now sells a higher-end Curriculum Intelligence tier. MagicSchool has added district-created student tools and deeper integrations. SchoolAI sells administrative alerts, session insights and rostering.

Existing education platforms have another advantage here. Instructure can put AI inside a larger Canvas contract. Pearson can tie AI to textbooks, MyLab, Mastering and Study Prep. Those companies already collect money from the institution, so AI can increase the value of an existing relationship instead of carrying the full sales burden itself.

Is school-paid AI tutoring stronger than parent-paid AI tutoring?

For broad K-12 deployment, school-paid AI tutoring looks stronger. Parent-paid tutoring remains attractive in narrower areas such as exam preparation.

Khanmigo shows why institutional distribution can be powerful. Newark Public Schools began with a limited Khanmigo pilot and has since expanded its broader Khan Academy partnership to reach about 29,000 students across 66 schools.

Khan Academy currently charges smaller U.S. schools $10 per student per year for its Enterprise Starter district product. The district receives much more than access to a chat window: teacher tools, student learning data, rostering and support sit around the AI experience.

The Newark case also gives us some outcome data. Khan Academy's three-year analysis covered about 8,000 students in grades 3 through 8. Students who reached Khan Academy's “Yearly Proficient Learner” threshold gained an average of six points on New Jersey's math assessment versus a statewide average gain of two points. The result measures intensive Khan Academy usage broadly rather than isolating the causal impact of Khanmigo alone.

That limitation is useful in its own way. Khanmigo becomes harder to separate from Khan Academy's content, exercises, teacher dashboards and implementation. For a school buyer, that bundle can be more useful than buying an independent chatbot.

Parents will still pay directly when the pain is acute enough. Medly's exam-prep growth is a good example. But asking millions of families to subscribe independently creates a much harder distribution problem than being deployed through the school a child already attends.

Is Canvas turning the LMS into the best place to sell education AI?

Canvas now has one of the strongest positions in education AI because Instructure can monetize new AI workflows inside software institutions already rely on every day.

Instructure has rolled out its three new Canvas tiers globally: Core, Plus and Next. Basic AI features such as discussion summaries, translation, course search and quiz-question generation sit inside Core. More valuable teacher workflows, including rubric generation and grading assistance, require Canvas Plus. Canvas Next adds the most advanced features, including an AI agent that can act across more than 500 Canvas APIs, natural-language data analysis and student study tools.

The commercial design is worth paying attention to. U.S. customers received temporary free access to premium IgniteAI features, and that period has now ended. Institutions that want to keep using the premium tools have to move to Plus or Next.

This is a very strong position. Canvas does not have to persuade a university to adopt another standalone AI product from scratch. It already holds the courses, assignments, grades, teachers, students and administrative permissions. Its AI can work on the actual course rather than asking users to rebuild context elsewhere.

The same advantage improves trust. A university can enable or disable AI at institutional, departmental or course level. Faculty can use AI inside SpeedGrader while keeping the final grading decision. Students can generate study materials from the course content their instructor actually assigned.

As AI capabilities become easier to copy, distribution inside the workflow becomes more valuable. The LMS is one of the places where that advantage is unusually difficult for a startup to reproduce.

If you want more recent data on this point, please see our latest AI in education market report.

Can OpenAI and other model companies sell AI directly to universities?

OpenAI has already shown that universities will sign very large contracts for general AI access, although the price per eligible user can become surprisingly low.

California State University is the clearest case. CSU has renewed ChatGPT Edu for another three years at $13 million per year after spending $17 million on its initial 18-month deployment. More than 470,000 students and roughly 63,000 faculty and staff are covered.

That works out to around $24 per eligible person per year using the current population, or roughly $2 a month. The contract therefore looks more like infrastructure pricing than a consumer ChatGPT subscription multiplied by half a million seats.

The renewal matters because CSU had enough experience with ChatGPT Edu to walk away and chose to continue. Yet adoption remains the obvious weakness. CSU reported more than 93,000 activated accounts during the earlier rollout, which was ahead of its internal launch expectations but still represented a minority of the total population covered by the agreement.

There is also genuine resistance. A CSU survey of more than 94,000 students, faculty and staff found widespread AI use alongside substantial skepticism about its educational benefits. Faculty opposition to the renewal has been public and organized.

Institution-wide AI licensing is clearly capable of generating substantial revenue for model companies today. Keeping those budgets several years from now will depend much more on usage, workflow integration and measurable educational value.

Is AI worth more as a feature than as a standalone education product?

For many education companies, AI currently looks more valuable when it improves a product people already pay for, because the company already owns distribution, context and monetization.

Pearson is a good example. More than three million higher-education students can now access its AI-powered learning tools through Pearson products. The AI sits beside the textbook or course material, which gives it trusted context and puts it directly inside an existing study workflow.

Pearson's recent learning research also gives the company a stronger sales story than “we added a chatbot.” Across nearly 80 million interactions involving close to 400,000 students, users of its embedded AI study tools were much more likely to engage actively with course material. A more recent Pearson study found students using AI-powered adaptive practice were 90% more likely to reach initial mastery of a topic than students using static practice, without spending more study time.

Instructure is taking a similar approach with Canvas. Advanced AI becomes one reason to move from Core to Plus or Next. Duolingo uses AI inside premium conversation experiences while benefiting from an enormous existing consumer funnel.

AI can improve the cost side too. Duolingo said earlier this year that it produced 20,500 new course skills in one quarter, compared with an average of about 7,100 per quarter during 2025 and 1,800 during 2024. That is an order-of-magnitude jump from two years earlier.

Embedded AI can therefore create value through higher-tier subscriptions, stronger retention, better learning outcomes or lower production costs. A standalone AI company has to build those advantages from zero.

Model	Where the advantage comes from	Current evidence
Pearson AI inside courseware	Trusted content and existing student access	3M+ students can access the tools; learning studies show stronger engagement and proficiency
Canvas IgniteAI	Existing LMS workflow and institutional contracts	Premium AI is now tied to Plus and Next tiers
Duolingo AI features	Huge consumer habit and subscription funnel	12.7M paid subscribers across Duolingo plans
Standalone generic AI tutor	Primarily model capability	Increasingly exposed to free general AI

Is workforce learning a better AI business than K-12?

Workforce learning currently has better gross-margin potential and clearer economic buyers than most K-12 AI products. Selling to employers is hardly effortless, though.

Coursera completed its combination with Udemy this year, giving us a useful view of the economics at scale. In the latest quarter, the combined company reported $140 million of enterprise revenue and a 79.3% enterprise gross margin. Consumer revenue was larger at $158.6 million, but consumer gross margin was lower at 65.1%.

The difficulty appears in retention. The combined enterprise customer base stood at 12,107, down 2% year over year, while net retention fell to 91%. Existing customers were collectively spending less than a year earlier after accounting for churn and contraction.

Companies clearly spend real money on workforce learning, and the economics can be attractive. The harder part is convincing employers to keep expanding those budgets.

AI still gives the category a strong reason to exist. Companies need employees to learn new tools and update skills faster than traditional training cycles allow. Coursera has responded by investing $100 million in LearnVector, the new AI-native learning company founded by Andrew Ng, while building more personalization and skills intelligence into its own platform.

If you want more recent data on this point, please see our latest AI in education market report.

Can an AI-native private school like Alpha actually make money?

Alpha School has clearly found families willing to pay premium prices for AI-centered education, but its rapid expansion still tells us more about demand than about long-term profitability.

Alpha is expanding aggressively. The network plans to reach roughly 50 campuses, and new locations have opened or been announced across cities including Raleigh, Santa Monica, Southlake, Boston and Houston.

The tuition is substantial. Alpha currently lists campuses around $40,000 in Austin and Texas markets, $50,000 in several cities, $65,000 in places such as New York and Boston, and as much as $75,000 in San Francisco and Palo Alto. Brownsville is the major low-price exception at $10,000.

The educational model compresses core academics into about two hours each morning using adaptive software, followed by workshops on communication, entrepreneurship, leadership and other skills. The economic question is whether that software-heavy academic block allows Alpha to operate a school materially more efficiently than traditional premium private schools.

We still lack the financial disclosure needed to answer that confidently. Alpha needs physical campuses, staff, admissions, local compliance and substantial real estate. One of its latest openings charges $45,000 per year in Raleigh, while a planned Houston-area campus occupies an 8,400-square-foot property. In Miami Beach, the group paid $19 million for a former Montessori campus.

Those are real school economics with real fixed costs.

The willingness to pay is impressive, and the pace of new openings is one of the freshest signs that some parents want this model. Alpha still belongs in the promising rather than proven category until we can see mature-campus enrollment, retention, operating margins and independent evidence that its academic claims hold across a much larger network.

Will AI education companies eventually get paid for outcomes instead of seats?

Outcome-based pricing could become one of the strongest AI education models, but the market currently lacks the measurement needed to make it common.

A school should care more about improving literacy than about generating 50,000 AI prompts. A parent cares about the exam result. An employer cares whether an employee acquired a useful skill.

We are starting to see vendors build the evidence layer required for that transition. Khan Academy now publishes district studies linking sustained practice with assessment gains. Pearson has analyzed tens of millions of interactions to measure changes in reading behavior and proficiency. MagicSchool currently highlights a district case where the share of students meeting literacy expectations increased by 28%. Brisk's two-year teacher study measures time savings and changes in workload rather than simple usage.

The problem is attribution. A student may improve because of the teacher, curriculum, tutoring, family support, extra practice or the AI product. Vendors would take substantial financial risk if payment depended on a metric they cannot fully control.

Near-term pricing will therefore remain mostly per student, per educator, per institution or per subscription. Outcomes will influence the buying decision long before they determine the invoice.

Which AI education business models are already looking weak?

The weakest AI education businesses today are the ones charging for capabilities that general AI platforms can reproduce almost immediately.

Generic homework chatbots sit at the top of that list. Google is giving students increasingly sophisticated study tools inside Gemini. OpenAI is tailoring ChatGPT directly for younger users. Microsoft is putting education-specific creation tools into Copilot. Competing mainly on the quality of a chat interface leaves little room for pricing power.

Single-purpose teacher generators have the same problem. Creating one lesson plan, quiz, rubric or reading-level adaptation was impressive three years ago. These days, every major AI system can do it. Brisk and MagicSchool are both moving into curriculum, analytics, student activity and district administration for exactly this reason.

Unlimited low-price AI usage can also produce ugly economics. Medly's switch to daily usage caps on its free tier shows that interactive tutoring and marking have a real variable cost. Heavy engagement is valuable only when pricing or infrastructure can support it.

Institution-wide seat licensing has a different risk: unused seats. CSU's contract proves that universities can spend eight figures on AI, while the gap between eligible users and activated accounts shows why future buyers will eventually demand stronger adoption evidence.

Finally, AI-generated educational content has become a poor moat by itself. Pearson, Duolingo, teachers and tiny startups can all generate far more material than before. The scarce assets are increasingly trusted curriculum, distribution, learner data, assessment, workflow and proof that the material actually helps someone learn.

If you want more recent data on this point, please see our latest AI in education market report.

So what business models are actually working in AI in education today?

The AI education business models working best today sell a valuable learning workflow, outcome or institutional layer around increasingly cheap intelligence.

The strongest consumer model is a premium AI layer attached to a learning habit or a high-stakes objective. Duolingo already owns the habit. Medly owns the exam context. Students have a clearer reason to pay because the product moves them through a defined journey rather than simply answering arbitrary questions.

In K-12, the most convincing startup model is currently free educator adoption followed by paid school or district deployment. Brisk, MagicSchool and SchoolAI have all moved toward this architecture. Teachers get useful productivity software with little friction; districts pay for curriculum alignment, student access, analytics, privacy, training and control.

Embedded AI inside existing education software may be even stronger. Canvas can now use advanced AI as a reason to upgrade the LMS. Pearson can put AI beside course material already assigned by instructors. These companies enter the AI race with distribution, context and purchasing relationships already in place.

Large university licensing is also real. CSU's three-year ChatGPT Edu renewal proves that a model provider can win an eight-figure institutional contract. The remaining challenge is proving that enough of the licensed population actually uses the product in ways the institution values.

Workforce learning has attractive economics because employers can connect education spending with productivity and skills. Coursera's latest enterprise gross margin shows that the model can be financially strong, although its 91% net retention shows that customers are still scrutinizing what they buy.

Specialized AI tutoring is working where the goal is narrow enough to measure. Exam preparation looks considerably more defensible than open-ended homework chat. School-funded tutoring also has an advantage when AI is integrated with curriculum, student data and teacher workflows.

AI-native private schools sit further out on the risk curve. Alpha's expansion and tuition levels prove willingness to pay. We still need mature economics before calling the model proven.

The biggest change since the first wave of education chatbots is that model access itself is losing commercial value very fast. The companies with the best position now own something around the model that remains scarce.

AI education business model	Our judgment today	What customers are really paying for
AI premium inside an established consumer learning product	Working strongly	Existing habit plus better learning features
Specialized AI tutoring for exams and other high-stakes goals	Working, still early	A defined outcome and cheaper alternative to human tutoring
Free teacher product → paid school or district platform	Working and getting stronger	Workflow, curriculum, governance and deployment
AI embedded in an LMS or existing courseware	Probably the strongest structural position	Distribution, context and an existing budget
University-wide foundation-model licensing	Commercially validated	Secure institutional AI infrastructure
AI-enhanced workforce learning	Working, with retention pressure	Skills tied to economic productivity
AI used to lower content and operating costs	Clearly working	Better economics even without a separate AI fee
AI-native private schools	Promising, still unproven economically	A completely redesigned education experience
Generic standalone homework or tutoring chatbot	Structurally weak	A capability increasingly available for free

Who is the author of this content?

NEW MARKET PITCH TEAM

We track new markets so founders and investors can move faster

We build living "market pitch" documents for emerging markets: AI, synthetic biology, new proteins, and more. Instead of outdated PDFs or hallucinated LLM answers, our clients get a clean, visual, always-updated view of what's really happening: key players, deals, regulations, and signals that matter. Learn more about us.