Grades 1–12 · Online and in Vienna
At Ainstein, children learn by tackling meaningful problems, making things and teaching one another. AI helps them attempt more ambitious work. Their questions, decisions and growing understanding guide it.
AI can help a child investigate a question, explore a design or build a working tool that would once have been beyond their reach. We use that opportunity to make the work more ambitious. The child chooses a direction, learns what the problem requires, tests ideas and decides what to do next.
This is agency: learning to turn an intention into action. It grows through experience: beginning with a question, finding help, working through a setback and seeing an idea take shape.
Reading, maths, science and creative work all have a place in this process. Focused practice builds the knowledge children need; projects give them a reason to use it. Peers bring other perspectives, and mentors help each child find a challenge worth attempting.
For parents, the important change is personal: your child can show what they tried, what they now understand and what they can tackle next. Confidence has something real behind it.
Choose your setting
The method stays consistent. The setting changes to fit the student, family, or school.
Close gaps in a specific subject with adaptive daily practice, a named mentor, and progress your family can see. Start with the diagnostic.
A complete Grades 1–12 program with adaptive academics, peer cohorts, seminars, life competencies, and diploma planning.
The complete Ainstein model in person at the Otto Wagner Areal, with a founding campus planned for September 2027.
Begin with a supported subject and grade band, adding the mastery map, adaptive practice, and review evidence while your teachers remain in control.
The starting point
One student needs the next challenge. Another needs a foundation from two years ago. The same assignment cannot be the right next step for both.
Grades travel between teachers. A precise record of what a child understands, where a misconception began, and what it blocks usually does not.
A missed step can remain hidden until later work depends on it. By then the child experiences the consequence without seeing the cause.
Ainstein begins with a diagnostic that turns the learning history into a map: what is secure, what is fragile, what is missing, and which next step would make the greatest difference. The plan starts from that evidence and changes as the child learns.
A secure foundation gives children more freedom to pursue a difficult question and make something of their own.
The learning model
Ainstein changes the path, not the standard. The next topic, amount of practice, and timing of review adapt to the learner. Explanation and application determine when the learning is secure.
The mastery map shows what is secure, what is fragile, and what the learner is ready to tackle next.
Practice responds to the student’s own attempts. The tutor asks for reasoning, offers the smallest useful hint, and returns to skills at deliberate intervals.
Students explain the idea to peers, answer questions, and apply it in a new context. A mentor reviews the reasoning and evidence before mastery is recorded.
Examples
Students use AI to transform difficult material into audio, maps, visual worlds, and musical interpretations. Then they return to the original, ready to read more closely and think more deeply.
Audio briefings, character maps, vocabulary, cultural context, workbook practice, and songs help students enter a medieval text before returning to the source.
A classical poem becomes several musical interpretations so students can hear mood, pressure, danger, and rhythm before analyzing the language.
What makes it school
The system remembers what is secure, what needs another pass, and which misconception appeared in the last attempt. Each session begins from that record.
One mentor follows the student over time, meets them every week, notices quiet struggles, reviews the evidence, and decides what happens next.
In fixed groups of five, students solve aloud, question one another’s reasoning, and teach back what they learned. The session leaves evidence a mentor can review.
See the work your child has made, the decisions they can explain and the skills they are ready to use again. Mentor feedback connects that progress to the next challenge.
“This week Emma became proficient in two-step equations, but still makes sign errors when variables appear on both sides. In her peer session, she explained one-step equations, answered Tom’s question, and left a worked example for mentor review. Next week we focus on delayed retrieval and independent problem solving.”
Rigor
Students move forward when they can retrieve an idea, explain their reasoning, answer questions, and apply it somewhere new.
The mastery map keeps fragile concepts in view until they become secure.
Students show how they reached an answer and respond when a peer challenges the reasoning.
Skills return after a delay and appear again in new contexts before the student moves forward.
Real work, mentor-reviewed explanations, and an evolving mastery map make progress readable.
Grades record a result. Ainstein also shows the thinking behind it: students asking sharper questions, connecting ideas across disciplines, and using AI without outsourcing their mind.
Ainstein uses MAP® Growth, an independent, nationally normed assessment used by millions of students across the United States.
Learning models
The important differences are practical: who sets the pace, how students learn together, what adults decide, and what counts as evidence.
| Classic online schools | The new AI schools | Ainstein | |
|---|---|---|---|
| The teaching | Recorded curriculum delivered through a term schedule | AI adapts an individual practice path | AI supports focused practice and ambitious projects; students learn to direct the tools and explain their decisions |
| The group | Scheduled classes and independent coursework | Individual AI work with adult supervision | Fixed five-student groups where students explain, question, and leave evidence a mentor can review |
| The pace | Course sequence follows the academic calendar | Adaptive pathways often optimized for acceleration | Progress follows durable mastery and readiness for the next dependency |
| Motivation | Deadlines, grades, and course completion | Progress mechanics, goals, and rewards | Growing competence, useful work, relationships, and increasing ownership (why) |
| The human | Subject teachers responsible for large course cohorts | Guides supervise AI learning blocks | A named mentor follows your child and approves every consequential decision |
| Proof | Report cards and course completion | Adaptive-platform progress dashboards | US national norms plus explanations, projects, and mentor-reviewed evidence |
| Screens | Video classes and independent courseware | Condensed individual AI work blocks | Defined screen-time caps with peer sessions, seminars, projects, and life offline (the day’s rhythm) |
Questions parents ask
The Online School pathway follows a structured American high-school curriculum: recognizable courses, credits, transcript-compatible records and a graduation audit. We are building the US diploma pathway with accreditation partners; until that is finalized we say exactly that, and nothing more. On a parent call we’ll show you precisely where the pathway stands today.
Yes, with honest planning. A US high-school diploma alone is not treated as equivalent to the Matura or Abitur; Austrian universities typically want the diploma plus around four AP exams, and language requirements apply. So for students targeting Europe, we plan exactly those AP exams into Years 11 and 12. The pathway is well-trodden; it just has to be planned from enrollment, not discovered in the final year. We plan it with you.
The tutor can only act through a fixed set of approved tools; it cannot roam. Every message is safety-screened first; anything concerning stops the tutoring and alerts a human mentor immediately. The AI never decides your child’s progress, never writes to their record, and parent reports are composed from structured learning data only, never from chat logs.
There are no ads, and student data is never sold or used for advertising. Ever.
Yes, that’s the point. Mentors run the weekly check-ins, approve every report, set up peer sessions, review the evidence those sessions produce, and make every consequential decision. AI supports practice and ambitious project work. Mentors help children build understanding, work with others and take responsibility for their decisions.
We give AI a clear role within a problem the child is learning to solve. It might help compare designs, explain an unfamiliar idea or generate code to test. Your child makes choices, checks what happens and explains the reasoning. A mentor checks the underlying learning through discussion, focused practice and a new problem that uses the same idea.
A polished result is one part of the story. You should also be able to see what your child now understands, which decisions they made and how their ability to work independently is growing.
It means learning to begin, make choices and follow through. A child might notice a problem, ask a useful question, find the knowledge or help they need and try a solution. They learn to listen to teammates, change an idea when the evidence changes and choose their next step. Mentors provide more structure for younger learners and gradually hand over responsibility as capability grows.
Both are the same case to us: the plan follows the child. We place by actual mastery, not birth year. A child with gaps works exactly at the edge of what they know; an advanced child moves on the moment mastery is proven, not when the calendar allows.
Through structured, supervised togetherness, and a lot of it. A normal week has two Socratic rounds and two to four peer-teaching sessions in a fixed group of five, plus group projects and cohort rituals: the same faces, week after week, which is how children actually form friendships. Younger children get more peer teaching and shorter seminars; teens get the full discussion dose. Sessions are structured, evidence is reviewed, and there is no unsupervised open chat, by design.
Three ways, and none of them require trusting us. First, MAP® Growth: the independent, nationally normed test used across US schools, taken two to three times a year, showing your child’s percentile and growth against millions of American students from a test provider we don’t control. Second, weekly evidence you can read: what was practiced, retrieved after a delay, explained in a peer session, and reviewed by the mentor. Third, ask your child to explain something they learned; with mastery-based learning, they can.
AI helps children attempt work that calls for more knowledge, more ideas or more technical ability than they could bring to it alone. They learn to direct those tools while developing their own understanding. A project can involve reading, measurement, coding, making and teamwork, with focused practice wherever a missing skill is holding the work back.
Our goal is for children to use increasingly powerful tools with increasingly capable minds. Their explanations, decisions and next attempts show what they are learning.
Learning Support is designed to cost less per month than a single weekly hour of private tutoring. Online School founding-family terms are shared on the parent call; we keep pricing conversations human because every family’s situation is different.
A weekly story in plain language: what your child worked on, what became solid, what’s still fragile, what they taught someone else, and what the mentor recommends next. Plus mastery progress you can read at a glance, and a one-click export of your child’s complete learning record, any time. It’s your data.
Founding families
A 20-minute conversation about your child, the available programs, and the clearest next step.
