AI makes 1:1 tutoring scalable.Universities should own it.
Students already use generative AI to learn, practice, and prepare for exams. OneTutor brings this layer into the university. Grounded in your course materials, controlled by lecturers, integrated with your systems.
The most innovative universities teach with onetutor
The most effective learning format was never scalable. Until now.
OneTutor supports participants exactly when they usually learn alone (after class, during self-study, before exams) based on the course materials you already have.
Bloom, 1984
The 2 sigma problem.
Students taught one-on-one outperform classroom peers by up to two standard deviations. The average tutored student scores better than 98% of students in a conventional classroom.
For 40 years this was education's holy grail. Proven to work, but impossible to fund. Generative AI now closes that gap.
Classroom average
1:1 tutored average
+2σ
Student performance →
PROBLEM
ChatGPT took over the learning layer.
Learning is increasingly hapening outside the university, but in tools the institution does not govern, cannot inspect, and cannot improve from.
UNCONTROLLED
Students learn in tools you don't govern.
External AI systems are't aligned with the course, the lecturer's objectives, or institutional quality standards.
UNGUIDED
Chatting is not the same as learning.
Generic AI gives answers, but doesn't scaffold understanding, motivate recall, or support exam readiness.
INVISIBLE
The feedback loop is broken.
Universities can't see where students struggle, which concepts fail, or how materials could improve.
AI-based learning is already happening. The questions is whether universities provide the environment or whether external platforms define it.
Why a chatbot isn't enough
Higher education needs learning infrastructure.
A chatbot can answer questions. Universities need governance, pedagogy, content grounding, assessment, analytics, and lecturer control working together.
Governance
GDPR, EU hosting, institutional control, no uncontrolled use of student data.
Pedagogy
Interactions designed to support learning, not just generate plausible answers.
Content integration
Grounded in the university's own course materials.
Formative assessment
Practice, quizzes, and learning checks that measure understanding.
Analytics
Visibility into where students struggle.
Personalized tutoring
Course-specific 1:1 support for every learner.
closed learning loop
Teach. Learn. Improve.
OneTutor creates a closed loop between lecturers, students, and course materials so teaching can improve over time.
01
Step 01
Teach
Lecturers set context, materials, and objectives.
The course owner defines what the AI tutor uses and what students should learn.
Course setup
Lecturer
Course materials
Learning objectives
Tutor instructions
Course tutor ready
02
Step 02
Learn
Students get course-specific tutoring & practice.
Grounded 1:1 support, quizzes and feedback. Available 24/7 in your environment.
Student
Can you walk me through this step by step?
Let us start from the definition in your lecture, then work through it together.
Grounded in your course materialsCan I try a practice question on it?
03
Step 03
Improve
What one asks improves the course for the next.
Questions, sticking points and quiz results come back aggregated. Lecturers sharpen the material, and the whole cohort gets the better version.
Course insights
Lecturer
Why does this step follow?
Can you explain the formula?
What is the difference here?
How do I start this task?
Aggregated for you
Most questions cluster on the same concept.
Worth revisiting in the next lecture
Generic AI gives answers. OneTutor creates a feedback loop.
Why not just let students use generic LLMs?
Generic AI tools
OneTutor
Learner-facing only
Lecturer-controlled and institution-owned
Generic or individually uploaded content
Grounded in official course materials
No curriculum alignment
Aligned with course objectives
No institutional feedback loop
Aggregated insights flow back and improve the course
Unclear data governance
Built for GDPR and university requirements
Every learner starts from zero
Every learner starts from what the cohort already worked through
Pilot
Start with one semester. Decide with data.
A pilot is not a free trial. It is a structured semester with onboarding for your lecturers, support while it runs, and a joint review at the end.
Setup and onboarding
Your existing materials go in. Your lecturers get an onboarding session with best practices from 800+ courses.
Use and support
Unlimited use within the pilot scope. We support adoption for lecturers and students and monitor usage together with you.
Review and decision
A joint review of usage data and feedback. Then a clear go or no go.
Either way, you finish the semester knowing which of your lecturers actually use AI, where the demand from your students really is, and how AI support is received in your teaching. That is a result even if the answer is no.
Talk about a pilot