Learning AI that schools actually trust.
We build adaptive tutoring, assessment, and curriculum AI for EdTech platforms, engineered for the strictest data protection rules around minors and aligned to the rubrics teachers actually use.
What we ship
into classrooms.
EdTech AI is more than a wrapper around ChatGPT. It needs to align to your curriculum, defer to your teachers, and protect your students. Every capability below ships with all three.
Adaptive Tutoring
Conversational tutors that explain step by step, recognize misconceptions, and stay inside your curriculum's scope.
Automated Grading
Rubric aware assessment for short answers, essays, and code, with rationales teachers can defend to parents.
Content Generation
Practice problems, lesson scaffolds, and differentiated materials, pinned to standards and reviewable by curriculum teams.
Learning Analytics
Mastery tracking, intervention signals, and instructor dashboards, without exposing individual student data outside the school.
Multilingual Curriculum
Same model, multiple languages, built for systems where instruction crosses dialects, scripts, and education ministries.
Student Data Protection
FERPA, COPPA, GDPR K aligned, no model training on student data, full erasure paths, and audit trails reviewable by the school.
Five buyers, one bar: AI that educators sign off on.
EdTech AI has two audiences to convince, and the harder one is not the student. These are the situations that bring product teams, publishers, and ministries to us, and what shipping looks like when teachers stay on board.
Adding AI to an EdTech Product You Already Run
The founder case: your platform works, your investors are asking where the AI is, and a thin ChatGPT wrapper would embarrass the brand in front of the schools that trust it. We build tutoring, grading, and content features that live inside your existing product and your existing compliance posture, differentiated by curriculum depth rather than by a chat window. And if the platform itself is still being built, our web and SaaS development team ships the product and the AI as one architecture instead of a bolt-on.
AI Tutors With Evidence Behind Them
The research finally caught up with the promise: a 2025 Harvard randomized trial found AI tutoring outperforming in-class active learning with effect sizes between 0.73 and 1.3 standard deviations, and World Bank trials measured gains equivalent to one to two years of schooling in low resource settings. Those results came from carefully designed tutors, not raw chatbots, which is the entire point. We build tutors that explain step by step, recognize misconceptions, and stay inside your scope and sequence, because the evidence says design is what separates the studies from the wrappers.
Grading That Teachers Can Defend
A teacher with 150 essays does not need AI opinions, they need consistent, rubric anchored first-pass grading with a rationale attached to every score, so the human review takes minutes instead of evenings and the grade survives a parent meeting. Our assessment systems grade against your rubric, show their reasoning, and flag the borderline cases for the teacher, which is the difference between saving teachers time and asking for their trust with nothing in return.
National and Ministry Scale Platforms
Education systems that cross provinces, scripts, and dialects need AI that was built for that reality, not translated into it as an afterthought. We build curriculum aware models for national platforms, exam boards, and public private programs, backed by our dedicated multilingual and low resource language AI practice, so the student learning in Urdu, Sindhi, or Arabic gets the same quality of tutor as the one learning in English.
Reading, Speaking and Oral Assessment
Some of the highest impact education AI never touches a keyboard: early readers practicing aloud, language learners drilling pronunciation, and oral exams that currently consume weeks of examiner time. Our speech AI practice powers listening and speaking features tuned for young voices and accented speech, which off the shelf speech APIs handle notoriously badly.
The Dependence Problem Is Real, and It Is a Design Choice.
A 2026 national survey found 95 percent of faculty worried that AI makes students dependent and dulls critical thinking, and the research backs the worry: badly designed tools produce answer copying, not learning. Our tutors are built the opposite way, asking before telling, requiring an attempt before revealing a step, coaching the struggle instead of erasing it, with teacher dashboards showing exactly where each student leaned on help. The goal is a tutor that makes itself progressively unnecessary, which is also the definition of good teaching.
Education Is Regulated Twice, and We Build for Both.
Education AI sits in a double frame: it is an Annex III high risk category under the EU AI Act, and it processes children's data, which carries heightened protection under GDPR, FERPA, and COPPA alike. Every platform we ship is architected inside both frames from day one through our GDPR compliant AI practice, so the procurement review at a school district or a ministry finds documentation, not surprises.
From standards to working classrooms.
Education AI fails when it's built without educators. We embed curriculum experts and teachers into the build from day one.
Curriculum & Rubric Mapping
We ingest your standards, rubrics, and pedagogical guidelines, defining what the AI is allowed to teach and how it should sound.
Educator in the Loop Build
Teachers grade the AI's outputs alongside their own. Their feedback drives tuning, not a generic AI evaluation.
Safety & Compliance Pass
Age appropriate filters, refusal patterns, parental consent flows, and audit logs, all reviewed before any student touches it.
Pilot to Scale
Phased rollout, single school to district to platform, with weekly review of accuracy, escalations, and student outcomes.
The evidence, the economics, and the dependence question, answered.
Education buyers have been burned by EdTech promises for two decades, so this page skips the promises. Here is what the research actually shows, what the systems actually cost, and the question every teacher asks first.
Yes, when it is built properly, and 2025 was the year the evidence became hard to argue with. A Harvard randomized controlled trial published in Scientific Reports found AI tutoring outperformed in-class active learning with effect sizes of 0.73 to 1.3 standard deviations, among the largest ever measured in education research. World Bank trials found gains equivalent to one to two years of schooling, and a systematic review of 28 K-12 studies covering 4,597 students found consistently positive effects. The honest caveats: results came from carefully designed, curriculum integrated systems, novelty effects need watching, and efficiency on a test is not the same as durable understanding. Which is why our pilots measure retention weeks later, not just scores on day one.
A single well built feature, an adaptive tutor for one subject or rubric based grading for one assessment type, typically runs $20,000 to $60,000, including the curriculum ingestion and educator review loop that separates it from a wrapper. A full adaptive platform spanning tutoring, assessment, content generation, and analytics runs $60,000 to $200,000 and beyond depending on subjects, languages, and compliance scope. The cost driver buyers underestimate: educator in the loop evaluation, which adds real weeks and real money and is also the single biggest predictor of whether teachers adopt the result.
The most legitimate objection in this field, and the one we design against rather than argue with. The failure mode is well documented: tools that hand over answers produce superficial learning, and most faculty rightly fear it. Our answer is architectural: tutors that require a student attempt before helping, reveal one step rather than the solution, escalate hints gradually, and log help seeking patterns to a teacher dashboard so overreliance is visible and correctable. We also give schools a lever we consider non negotiable: teachers can tune how much help the tutor gives, per class and per assignment, because pedagogy decisions belong to educators, not to our defaults.
Less than most buyers expect, and it matters enormously for per seat pricing and ministry budgets. A typical tutoring session costs cents in model and infrastructure spend, and rubric based grading runs pennies per submission, which at scale prices out to a small fraction of the per student cost of the human alternatives it supplements. Every proposal includes a projected cost per student per month at your usage patterns, because education buyers budget per head, and an AI feature whose unit economics are a mystery is a feature procurement will not approve.
A focused single feature ships to a supervised pilot in 6 to 10 weeks, including the curriculum mapping and the safety pass. Full platforms run 3 to 6 months. The stage we refuse to compress is the educator evaluation loop, where teachers grade the AI's outputs alongside their own before any student sees it, because two extra weeks there buys the adoption that decides whether the whole project succeeds. Rollout then follows our phased path, one school, then a district, then the platform, with outcome data reviewed at each gate.
You do, across all three layers. The models tuned on your curriculum and rubrics are your asset, portable and documented. The practice problems, lesson scaffolds, and assessments the system generates are your intellectual property, reviewable and editable by your curriculum team. And the learning analytics belong to you and your schools under the data processing terms your compliance bar requires. We build education AI as a capability you own, not a subscription you rent, because ministries and serious EdTech companies do not build national infrastructure on someone else's black box.
Bring one unit of your curriculum and one rubric to the call. We will show you what a tutor and a grader built on them would look like, and your teachers can judge the output before you spend anything.
Questions about
EdTech AI Platforms
Yes. We ingest your standards, scope and sequence, and rubrics, and we use a combination of retrieval and tuning so the AI stays inside your curriculum.
No. Student data is processed under contract for the educational service only, never used to train models. Erasure paths and audit trails make this verifiable.
Rubric aware grading with reviewable rationales, calibration against teacher graded samples, bias evaluation across student demographics, and a standing teacher override path.
Consent flows for parents and guardians, age verification gates, and content filters tuned to age appropriate output. We design to FERPA, COPPA, and GDPR K together.
Yes, Canvas, Moodle, Schoology, Google Classroom, and most custom LMSes via LTI or REST. We do bidirectional sync of rosters, gradebooks, and assignments.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. We'll review your curriculum and student data setup, and tell you what's possible inside your compliance bar.
info@croncore.com