Lesson 1.1: What is Artificial Intelligence – history and evolution.

Lesson 1.2: Difference between AI, Machine Learning, and Deep Learning.

Lesson 1.3: General applications of AI in daily life and extrapolation to aviation.

Exercise: Identify examples of AI in the daily routine of a pilot.

Lesson 2.1: Digital transformation of the aviation industry.

Lesson 2.2: Big Data in aviation: managing thousands of data points per flight.

Lesson 2.3: Aviation cybersecurity and data protection.

Case Study: How Airbus and Boeing integrate AI into their operations.

Lesson 3.1: Pilot assistance systems powered by AI.

Lesson 3.2: Workload management in the cockpit.

Lesson 3.3: Intelligent automation during takeoff, cruise, and landing.

Simulation: Using a virtual AI copilot in IFR flight.

Lesson 4.1: Advanced weather forecasting with AI.

Lesson 4.2: Turbulence and severe weather detection.

Lesson 4.3: Planning safer and more efficient flight routes.

Case Study: How AccuWeather and NASA use AI in weather prediction.

Lesson 5.1: Smart sensors in aircraft.

Lesson 5.2: Machine Learning to prevent technical failures.

Lesson 5.3: Reducing operational costs through predictive maintenance.

Lesson 6.1: Optimizing slots and routes with AI algorithms.

Lesson 6.2: AI for collision avoidance and separation improvements.

Lesson 6.3: Virtual towers and computer vision for ATC.

Simulation: How an AI-based ATC system manages 200 operations per hour.

Lesson 7.1: Facial recognition and biometric systems for passengers.

Lesson 7.2: Baggage handling with AI.

Lesson 7.3: Use of drones and robots in airport operations.

Case Study: Singapore and Dubai airports as pioneers in AI.

Lesson 8.1: Will AI replace pilots? Myths vs. reality.

Lesson 8.2: Technical and legal limitations of AI in aviation.

Lesson 8.3: The future: from AI copilots to autonomous aircraft.

How to use ChatGPT / AI copilots to:

Create quick route briefings (airports, NOTAMs, frequencies).

Summarize aircraft manuals in seconds (abnormal/emergency procedures).

Clarify ATPL concepts in simple language before exams.

Hack: Copy a METAR and ask AI to translate it into plain English with operational recommendations.

Insert METAR/TAF into AI to obtain a visualized summary of weather conditions.

Use AI-powered apps to transform raw data into interactive turbulence, visibility, and storm maps.

Hack: Ask AI for a probability of cancellation or delay based on forecast data.

AI copilots (iPad apps) for voice checklists.

Quick AI explanations of ECAM/EICAS alerts.

Hack: Offline AI assistant that translates cockpit warnings into plain language during flight.

AI as a virtual ATC to practice phraseology in English.

Correcting pronunciation with AI-powered speech tools.

Hack: Train emergency ATC scenarios with AI to avoid communication block under stress.

Use AI to calculate real fuel burn and suggest better altitudes or power settings.

Compare fuel and handling costs across airports.

Hack: Ask AI “give me the most fuel-efficient route considering wind and reserves” based on your flight plan.

AI apps to monitor pilot fatigue using sleep/circadian data.

AI recommendations for optimal rest hours before long-haul flights.

Hack: AI logbook analysis to flag overwork and fatigue risks.

AI as a virtual ATPL simulator:

Solve performance and mass & balance problems step by step.

Generate custom EASA-style exam questions.

Hack: Ask AI to run a mock airline interview or oral exam with real-time feedback.

AI logbook analysis for trends in fuel consumption, weather, or flight performance.

Automated safety reports for SMS systems.

Hack: Export GPS flight data and ask AI to generate a visual route analysis with improvement points.