البرمجة والذكاء الاصطناعي (اختياري مسار الهندسة) — الصف الثاني الثانوي

Each lesson follows the book's map (big idea, key concepts, key terms, worked example, «توقف وفكّر», exam-style question); the academy adds hands-on labs on synthetic data and defensive-security scenarios.

Estimated duration: full year ≈ 30 weeks (23 academy lessons + labs and project weeks)

What you will learn

• Trace the main stages of IT (PCs → web → smartphones → cloud, big data and AI), explain Moore's law and emerging technologies (edge computing, autonomous driving, AR / VR, quantum computing) and analyse social impact with evidence
• Explain how AI learns from data, describe its uses in daily life and industry, and evaluate its ethical issues (bias, privacy, transparency, accountability, jobs)
• Explain how HTTPS combines symmetric and public-key encryption, TLS handshakes and certificates; apply multi-factor authentication; describe network-security design and incident response and risk management
• Describe web-application architecture (client, server, database, front end / back end), HTTP / HTTPS, GET / POST, status codes, APIs and JSON, and front-end technologies (HTML, CSS, JavaScript)
• Choose media types by their properties (resolution, colour depth, sampling, compression), design information and user experience, evaluate websites (usability tests, heuristics, analytics) and improve them iteratively
• Distinguish primary and secondary data, plan sampling (simple random, stratified) and avoid bias; clean and transform data (missing values, duplicates, outliers, formats); use open data and APIs responsibly
• Apply statistical inference: point and interval estimates, confidence intervals, null / alternative hypotheses, p-values and significance levels, Type I / II errors, and the choice between t- and chi-square tests
• Fit and evaluate simple and multiple regression (least squares, R², RMSE, MAE), distinguish correlation from causation, and communicate data with honest visualisations
• Explain machine-learning basics: features and labels, training / test split, supervised vs unsupervised learning, overfitting, evaluation (accuracy, precision, recall)
• Explain neural networks (neurons, weights, bias, activation, layers) and deep learning, and how large language models and generative AI work, with their limits and responsible use

Who it is for: Engineering-track Baccalaureate S2 students who chose Programming & AI as their elective

Prerequisites: S1 Programming & AI (CRS-S1-PROGAI) and S1 mathematics