1:1 tutoring for a handful of students.
Scarce
Before
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.
One-on-one tutoring has always been one of the most effective ways to learn, but impossible to provide for every student. Generative AI changes that.
A learning layer for every student.
Scalable
Now
The problem
ChatGPT took over the learning layer.
Learning is increasingly happening 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 aren'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 question 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.
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.
The closed learning loop
Instruct. Learn. Verify.
OneTutor creates a closed loop between lecturers, students, and course materials so teaching can improve over time.
01 · INSTRUCT
Lecturers set context, materials, and objectives.
The course owner defines what the AI tutor uses and what students should learn.
02 · LEARN
Students get course-specific tutoring & practice.
Grounded 1:1 support, quizzes and feedback. Available 24/7 in your environment.
03 · VERIFY
Interactions become measurable learning signals.
Questions, struggles and quiz results flow back as anonymized insights for teaching.
Generic AI gives answers. OneTutor creates a feedback loop.
The product
A learning system, not a list of features.
Course-specific AI tutoring
Every student gets 1:1 support grounded in the actual lecture materials.
Practice & active learning
Quizzes, repetition, feedback, and exam preparation. From passive reading to active understanding.
Lecturer-controlled content
Lecturers set objectives, curate materials, and keep control of what the AI tutor uses.
Learning analytics
Aggregated insights show which concepts need attention.
LMS / LTI 1.3
Runs alongside Moodle, ILIAS, Canvas.
SSO / SAML
Shibboleth, Azure AD, Keycloak.
EU hosting & GDPR
German residency, privacy by design, AVV ready.
OneTutor vs. generic AI
Why not just let students use generic LLMs?
Generic AI tools
Learner-facing only
Generic or individually uploaded content
No curriculum alignment
No institutional feedback loop
Unclear data governance
Answers questions
OneTutor
Lecturer-controlled and institution-owned
Grounded in official course materials
Aligned with course objectives
Aggregated learning insights flow back
Built for GDPR and university requirements
Supports learning, practice, and improvement
Research & evidence
AI in teaching should be measured, not marketed.
Together with the bidt (Bavarian Research Institute for Digital Transformation), OneTutor is part of AIffectiveness. A three-year accompanying study across ten universities in Bavaria.
Explore the researchAIffectiveness
Three-year accompanying study with the bidt across ten Bavarian universities.
Real course settings
Evaluation in varying teaching environments: different subjects, cohort sizes and levels.
Learning impact
Effectiveness, engagement, exam preparation, and teaching feedback.
Responsible implementation
Privacy, didactics, and lecturer control as core design principles.
Bring AI-based learning
into your university.
Grounded in your courses, controlled by lecturers, integrated with your systems, and visible through learning insights.
GDPR compliantSSO readyLMS / LTI ready