BLOCK 1 · HERO

Master artificial intelligence. Build an AI project that truly impresses

Stage 2 of the AI-SANA program was developed under the academic leadership of Paul Kim, PhD — Advisor at Lumos Capital Group, Member of the National AI Council under the President of the Republic of Kazakhstan, ex-Associate Dean and CTO at Stanford University Graduate School of Education

The world is changing faster than textbooks can be updated. AI is already transforming mining, agribusiness, healthcare, and finance — right here in Kazakhstan. Program 1 gives you the foundation: technology literacy, entrepreneurial thinking, and direct contact with those who are building the future right now.

8 weeks. 15 hours per week. Hands-on from day one.

The program is open to students of Kazakhstani universities · Free of charge

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BLOCK 2 · WHO IS THIS FOR

The program was built for those who want to take action

No technical background required. Just the desire to learn and the readiness to work.

Program 1 is right for you if:

• You are a student at a Kazakhstani university and want to understand how AI will change
  your profession and industry.
• You are interested in entrepreneurship but don't know where to start.
• You want to work with real tools — code, platforms, case studies — not just theory.
• The context of Kazakhstan and Central Asia matters to you: local markets, local
  challenges, regional opportunities.
• You are ready for teamwork and critical thinking.

The program will demand effort:
This is not a course for passively watching videos. Every module includes practical assignments, team projects, and assessment on the SMILE platform. The final capstone is a full venture plan developed by a team.

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BLOCK 3 · PROGRAM OUTCOMES

What you will gain from Program 1

A practitioner-level understanding of AI

From foundational concepts to writing real Python code in Google Colab. You will understand how machine learning algorithms, neural networks, and language models work — and be able to apply them to real-world problems.

An entrepreneurial lens for spotting opportunities

Through direct conversations with venture investors and founders, you will understand how the venture capital market works, how to evaluate an idea before building it, and how decisions are made on the other side of the pitch.

Industry knowledge where opportunities are born

The program covers 8 key industries of Kazakhstan and Central Asia. You will hear from practitioners about where AI is already creating value — and where no one has reached yet.

A complete business  plan as your final deliverable

The capstone project is a fully developed startup concept: 240 critical questions across 12 phases and a structured plan evaluated by an AI system. The best projects are published in the Peer Gallery.

Alem.ai Residency

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BLOCK 4 · ACADEMIC DIRECTOR

Under the guidance of Paul Kim, PhD

Advisor at Lumos Capital Group · Member of the National AI Council under the President of the Republic of Kazakhstan · Ex-Associate Dean & CTO, Stanford University Graduate School of Education · Ex-Chair, International Expert Committee of the World Bank.

The architecture of Stage 2 of the AI-SANA program is based on the principle of Never Assume — a key idea from Stanford's design thinking methodology. The premise is simple: before building anything, you need to honestly understand reality. Talk to people. Make sure you are solving the right problem. This is precisely the principle that separates startups that survive from those that don't.

In the program, Paul Kim personally leads the industry module: he introduces each topic, explains the strategic logic behind the choice of sectors, and helps students identify competitive advantages in the context of Kazakhstan and Central Asia.

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BLOCK 5 · METHODOLOGY

How the program is structured

Every design decision in AI-SANA is grounded in educational technology research accumulated at Stanford over more than two decades.

Understanding precedes creation

Before approaching code or a startup idea, the student builds contextual understanding. Modules progress from the general to the specific: first a picture of the world, then the tools.

AI augments human thinking — it does not replace it

The program teaches students to use AI as a tool for amplifying their own thinking. From the very first sessions, students work with ChatGPT and Claude as working partners — for debugging code, testing hypotheses, and getting feedback.

Collaborative learning outperforms individual learning

Research shows that three people working on the same task significantly outperform those working alone. The entire assessment and project structure of the program is built on this principle.

The L-E-L-E pattern

Each topic follows the cycle: Lecture → Experiment → Lecture → Experiment. Theory is reinforced through immediate practice. The student doesn't just listen — they change parameters, observe results, ask questions.

The SMILE Platform

Stanford Mobile Inquiry-based Learning Environment — a platform developed at Stanford for large-scale education without sacrificing quality. All assignments, exams, and the capstone are completed on SMILE. The AI system evaluates work using Bloom's Taxonomy and provides personalized feedback.

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БЛОК 6 · ПРОГРАММА — МОДУЛИ И СПИКЕРЫ

Семь модулей. Десять недель.

От фундамента к применению: технологии и предпринимательство → отраслевые кейсы → собственный стартап-проект.

Модуль 1.1 Неделя 1 · ~6 часов

Введение и диагностика

Программа начинается с диагностики: ИИ-система оценивает текущий уровень знаний и помогает сформировать персональный учебный маршрут. Модуль закладывает культуру совместной работы, которая будет сопровождать студентов до финального капстоуна.

Основные темы

• AI-based диагностика стартового уровня и формирование персонального маршрута
• Ориентация в платформе SMILE: структура, навигация, правила работы
• Академическая честность: принципы и практика
• Формирование команд (3 человека) и правила командной работы
• Обзор всей программы: что будет в каждом модуле и зачем

Формат: видео-введение · диагностический тест на SMILE · знакомство с командой

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Module 1.2Weeks 1–2 · ~30 hours

Introduction to Artificial Intelligence

The technical and conceptual foundation of the entire program. The module opens with context rather than abstractions: AI is already at work in Kazakhstan's agriculture, healthcare, finance, and education. The student understands why this matters personally — and only then begins working with the tools.

All exercises are tied to specific Kazakhstani sectors: crop disease detection on farms, fraud in payment apps, forest fire prediction, customer segmentation, academic performance analytics.

Key topics:

• The AI ecosystem: Hugging Face, Kaggle, GitHub — where real models and data live
• First code in Google Colab: a particle swarm governed by three rules — complex behavior from simple principles
• Supervised machine learning: classification, k-NN, decision trees, random forests, regression
• Application: crop disease detection on Kazakhstani farms
• Unsupervised learning: k-Means clustering, data segmentation
• Working with pretrained Hugging Face models: NLP tasks without training from scratch
• ChatGPT API: integrating a language model into your own code
• RLHF: how ChatGPT and Claude were trained on human feedback
• Algorithmic bias: why a technically correct model can be ethically problematic
• Algorithm selection framework: from task type to the right method

Format: video + experiments in Google Colab (L-E-L-E) + team discussions + exams on SMILE

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Module 1.3Weeks 2–4 · ~30 hours Introduction to Entrepreneurship

Introduction to Entrepreneurship

The technical and conceptual foundation of the entire program. The module opens with context rather than abstractions: AI is already at work in Kazakhstan's agriculture, healthcare, finance, and education. The student understands why this matters personally — and only then begins working with the tools.

All exercises are tied to specific Kazakhstani sectors: crop disease detection on farms, fraud in payment apps, forest fire prediction, customer segmentation, academic performance analytics.

Key topics:

• The mechanics of venture capital: how the VC market works, where the money comes from, who makes the decisions
• Investment criteria: how a startup is evaluated at an early stage
• Market sizing and validation: TAM/SAM/SOM, unit economics, speed
• Startup traditional business: the fundamental difference from an investor's perspective
• The founder's journey: pivot, failure, restart — real stories
• Venture-backed entrepreneurship in the AI era: how AI changes the speed and cost of building a company
• 5 criteria for finding an AI opportunity in any industry
• How to enter the venture ecosystem without connections or starting capital

Format: video interviews with investors and founders + case study analysis on SMILE + team discussions + exams

Module 1.4 Weeks 4–6 · ~25 hours

AI Trends and Design Thinking

The module is built around three expert practitioners, each bringing a fundamentally different perspective on AI innovation: technical depth, an understanding of capital and markets, and entrepreneurial execution. Together they provide a complete picture of what it takes to build a successful AI venture.

The module includes a comparative analysis of the two dominant global AI models — American and Chinese — as a strategic exercise: finding Kazakhstan's unique place within this landscape.

Key topics:

• The Never Assume principle: why the most important thing is to test rather than assume
• The global AI landscape: Silicon Valley China — different models, different strategies
• Kazakhstan's positioning in global AI: competitive advantages and the window of opportunity
• How a VC evaluates an AI startup: the investor's key questions about accuracy, defensibility, and inference costs
• Design thinking as an entrepreneurial tool: Stanford school methodology
• Empathize → Define → Ideate → Prototype → Test: the cycle applied to real startup examples
• Entrepreneurial frameworks in the AI era: pivots, failures, unicorn exits

Format: video lectures + China vs. Silicon Valley analysis + team projects + exams on SMILE

Module 1.5 Weeks 5–7 · ~35 hours

Industry Case Studies and Real-World Examples

The module is built entirely on direct conversations with investors and founders working in specific industries. Industry knowledge — where the pain points are, who makes decisions, what data is available, what regulatory barriers exist — is tacit knowledge that cannot be conveyed through a textbook.

The structure and synthesis of the module are provided by Paul Kim: his opening and closing sessions teach students to view different industries through a single analytical lens and extract transferable principles.

Key topics — common to all industries:

• Venture logic in a specific vertical: why an investor enters or passes
• Stakeholder mapping: who uses the product, who makes the purchase decision, who pays
• Industry data specifics: availability, quality, regulatory constraints
• Barriers to entry and moats: what makes an AI company defensible against copying
• Bridge founders: why insider industry knowledge is a structural competitive advantage
• Selling outcomes, not technology: how to sell the result rather than the technology

Format: video sessions + analytical assignments + team discussions + exams on SMILE

Module 1.6 Week 7 · ~20 hours

Design Thinking for AI Innovation

The most hands-on module in the program. Students complete the full Stanford d.school cycle — Empathize → Define → Ideate → Prototype → Test — applied to their own startup idea. The Never Assume principle here is not read about but lived through in practice.

The module teaches students to work with uncertainty: a prototype should match the level of understanding of the problem, not design ambitions. At a stage of high uncertainty, paper sketches and verbal descriptions are more valuable than polished presentations.

Key topics:

• Empathize: observing and interviewing real users, documenting insights
• Define: formulating the problem from the user's perspective, Point of View Statement
• Ideate: idea generation methods — brainstorming, SCAMPER, worst possible idea
• Prototype: the principle of matching the prototype to the level of understanding — paper prototype vs. digital
• Test: how to test hypotheses quickly and cheaply, failure budget
• AI as a partner in the design process: idea generation, feedback analysis, iteration
• What an investor sees in a prototype: signals of team maturity red flags

Format: workshop (full design thinking cycle) + prototyping + team sessions + exam on SMILE

Module 1.7 Week 8 · ~20 hours

Capstone — Your Startup

The final project is a synthesis of everything studied throughout the program. Teams develop a complete startup concept through two sequential stages on the SMILE platform.

• The Swiss Cheese Model: how vulnerabilities across different dimensions of a venture lead to failure
• 12 key phases of startup development: vision, market, user, business model, team, finances, and others
• Principles of formulating strong questions: analysis vs. reproduction
• Venture Plan in Project Mode: a structured plan across 12 phases

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BLOCK 7 · INDUSTRIES

Eight industries. Real Kazakhstani context with the potential for global scale

The program deliberately selected industries in which Kazakhstan and Central Asia hold natural advantages — and where AI has yet to realize its potential.

Mining & Natural Resources · Agribusiness & Food · Healthcare · Data Centers & AI Infrastructure · Construction · Energy · Fintech · Education

Plus four cross-industry domains: Robotics & Automation · HR & Talent Management · Media, Marketing & Sales · Security

Kazakhstan is one of the world's leaders in mining, a major energy producer, and a fast-growing market with a young, tech-savvy population. The program helps students see these factors not as limitations, but as competitive advantages for building globally significant companies.

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BLOCK 8 · CALL TO ACTION

Start Program 1

Registration is open to students of Kazakhstani universities.

AI is not waiting for you to feel ready. Program 1 is your chance to master the technology, meet the people building it, and come out with a real project in hand.

● Top-performing students upon completion will be admitted to the next, more advanced program