Generation AI High School Curriculum

The curriculum is fully integrated into the national education system, ensuring nationwide access to AI education.

What makes Generation AI’s curriculum unique?

Advanced Mathematics Foundation

The curriculum is fully integrated into the national education system, ensuring nationwide access to AI education.

Project-Based Learning

Across grades 10–12, the curriculum integrates extensive Project-Based Learning (PBL) in math, Python, and AI, enabling students to apply theory to real-world problems while strengthening critical thinking and problem-solving skills.

From Users to Creators

The program moves from AI literacy and ethics to advanced creation of ML, DL, NLP, and LLM models. By graduation, students can build their own AI models, understand the underlying mathematics, and compete in international AI olympiads without extra tutoring.

Global Expertise & Best Practices

The curriculum was developed by a diverse team of international experts, with contributions from leading global companies, incorporating worldwide best practices to ensure a high-quality and industry-relevant educational experience.

Generation AI Curriculum

10th Grade

Advanced Algebra

Computer Science (Basic Level)

Machine Learning (Middle Level)

11th Grade

Advanced Algebra

Computer Science (Advanced Level)

Machine & Deep Learning (Advanced Level)

12th Grade

Advanced Algebra

Deep Learning (Advanced Level)

AI Apps (Implementation & Presentation)

Extra Clubs

Applications of Algebra, Python & AI in Real Life

English

Skills Development & Career Guidance

Unique Teaching Methodology

Each topic is taught through concrete, customized methods, making lessons more purposeful and engaging. Students develop the ability to transfer skills between abstract mathematics and applied AI, strengthening their problem-solving flexibility. The structured guidance reduces ambiguity, helping students gain confidence and autonomy in tackling complex concepts. By recognizing connections between algebra and AI early on, students approach problems as researchers and creators rather than passive learners.

Soft Skills

Mindset: Analytical, critical, systemic, creative, and innovative thinking.

Personal Skills: Attention to detail, curiosity, independence, and adaptability.

Teamwork: Communication, collaboration, organizational and planning abilities.

Leadership: Decision-making, leadership, and strategic/forward-looking thinking.

Hard Skills

Mathematics and Statistics: Mathematical thinking, numerical and statistical analysis, and modeling.

Data Science and AI: Data collection and analysis, machine learning, and AI models (Regression, Decision Tree, CNN, NLP, Transformers).

Programming: Python, data structures and algorithms, version control, and debugging. Tools: Colab, Jupyter, Scikit-learn, NumPy, Matplotlib, PyTorch, TensorFlow.

Presentation and Projects: Slide presentations, documentation, hosting, and project organization.

Teaching Resources