The principle
AI is not the product. Better learning is the product.
The goal is not to add AI decoration. The goal is to make explanations clearer, practice more active and course materials easier to navigate independently.
For lecturers, universities and teaching centers
Turn lectures into interactive learning systems
Websites, quizzes, practice tools, simulations and course hubs built around your real materials — with AI serving the pedagogy, not replacing the teacher.
Work in English, Hebrew and Russian · Start with one lecture or module
Learn → Understand → BuildAI tools organized around a real learning journey.
260K+YouTube subscribers
33K+followers on Stepik
6published Stepik courses
3working languages: English, Russian and Hebrew
What you receive
Concrete learning assets, ready to use
Each project is built from existing teaching materials and can start small.
Course hub
A clear entry point with modules, videos, tasks, deadlines and links.
Interactive lesson
A focused page that combines explanation, examples and practice.
Quiz or trainer
Immediate feedback, self-check and repeated practice.
Simulation
Sliders, graphs and visual experiments that build intuition.
Instructor kit
Reusable materials, guidance and a structure that can be updated.
Student journey
A visible path from first contact to confident application.
Flagship case
One lecture becomes a complete learning experience
A statistics or Data Science lecture can be redesigned as a connected system rather than a pile of files.
1
Start from the real lectureSlides, exercises, common mistakes and assessment goals.
2
Design the learning pathWhat students should see, try, check and revisit.
3
Build the systemWebsite, quiz, simulator, visual explanation and course map.
4
Test and improveUse student questions and behavior to refine the materials.
Real examples
Four kinds of interactive teaching work
Selected examples from live course systems and educational experiments.
Course hubs
Structured entry points for modules, videos, assignments and exam preparation.
Open Data Analysis hub →
Quizzes & trainers
Self-check tasks with immediate feedback for Python, probability and statistics.
Open Python example →
Simulations & visualizations
Interactive graphs and experiments that make abstract ideas concrete.
Open Time Series example →
Creative learning formats
Songs, visual stories and game-like tasks that support memory and engagement.
Watch an example →
NLP course panel
Probability control center
MLE quiz
Hypothesis testing
Statistical errors
Course task pageLive educational case
Data Analysis in the Age of AI
The free Russian-language course is an active testing ground for interactive course pages, quizzes, recordings, practice and community support. Materials are already available and can be studied at any time.
Process
A small pilot before a large transformation
One lecture, one recurring student difficulty or one course module.
A working page, quiz, visualization or trainer — not a slide deck about possibilities.
Check clarity, navigation and usefulness with real learners or the academic team.
Turn the successful prototype into a reusable course system.
Ways to work
Choose the smallest useful format
Workshop
A practical session for lecturers using their own course materials.
One-module pilot
A focused prototype for one lecture or unit.
AI Learning Kit
A connected set of pages, quizzes, simulations and teaching guidance.
Consultation
Review an existing course and define a realistic improvement plan.
Student feedback
Evidence from real teaching
“Thank you for your dedication, the exercises and the wonderful explanations. The quiz panel and engaging YouTube videos made the material clearer and more enjoyable.”Students of the Probability Course, 2025–2026
“Thank you for the semester, your patience, warmth and genuine desire to teach us and expand our knowledge. We greatly enjoyed learning with you.”Students, July 14, 2025 · translated from Hebrew
About
Dr. Igor Kleiner
Ph.D. in Statistics (2015–2020), Senior Lecturer at HIT and External Lecturer at the University of Haifa. Professional experience includes Microsoft, Intel, Skype and Huawei.
University teaching includes Data Science, statistics, probability, machine learning and programming; the lab grows out of tools and formats tested in real courses.
Author, with Henk Tijms, of “Surprises in Probability — Eighteen Short Stories” (2025). The teaching work combines academic rigor, industry experience and clear explanations.
In 2020–2021, he taught Data Science intensives for researchers and doctoral students in the University of Haifa’s Beit HaShita program, and in 2022 taught 13 hours at the two-week SICSS-Haifa summer institute. In 2026 he presented at IICDHSS at the Technion and at ICECA; his current research pipeline includes one active arXiv preprint and seven additional journal submissions.
FAQ
Common questions
Does AI replace the lecturer?
No. The lecturer remains responsible for meaning, judgment and the learning relationship. AI is used to accelerate production, variation and interaction.
Can we start with one lecture?
Yes. A small, clearly defined pilot is usually the best first step.
Can existing materials be used?
Yes. Slides, recordings, assignments and recurring student questions are the starting point.
Which languages are available?
Projects and workshops can be delivered in English, Hebrew and Russian.
Where are the tools hosted?
A project can be deployed as a standalone website or integrated into the institution’s existing learning environment, depending on technical constraints.
Contact
Discuss a pilot
Send a short description of the course, audience and difficulty you want to solve. The first response will focus on a realistic next step.
Direct email: igkleiner@gmail.com