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Aviation and artificial intelligence: what is in service, what is certifiable, what is coming

Where machine learning already works in aviation, how regulators assure it, and which plans are still proposals or research

By Aerospace Garage Desk · Introductory · 50 minutes · 24 narrated pages · Updated October 2026

What you will be able to do

  • ✓ Describe where machine learning is used in aviation today and sort any claim into in force, proposed or research
  • ✓ Explain EASA's AI roadmap, concept paper, AI levels 1A to 3B and the NPA 2025-07 proposal
  • ✓ Explain the FAA roadmap principles, the role of ICAO safety management, and how the EU AI Act reaches aviation through Article 108 and the 2026 Digital Omnibus
  • ✓ Explain why data quality, learning assurance and in-service monitoring decide whether a learning system can be certified
  • ✓ Apply human factors principles to explainability, human-AI teaming and responsibility
  • ✓ Map who builds and feeds aviation AI tools across manufacturers, airlines and maintenance organisations, and read dated trends critically

Course content

Session 1 · Where AI is actually used today

1.1

  1. Three questions for every AI claim · free preview
  2. Maintenance: predicting failures and reading images
  3. Operations, air traffic and airports
  4. Worked example: reading a predictive model's scorecard
Session 2 · EASA: roadmap, concept paper and AI levels

1.2

  1. From roadmap to guidance
  2. Six levels of AI
  3. From guidance to law: NPA 2025-07
  4. Worked example: classifying a traffic-detection assistant
Session 3 · The FAA, the EU AI Act and ICAO

1.3

  1. The FAA roadmap
  2. The EU AI Act and aviation
  3. ICAO and global standards
  4. Worked example: sorting an airline's AI tools under the AI Act
Session 4 · Data quality, learning assurance and governance

1.4

  1. The data is part of the design
  2. Learning assurance and certification limits
  3. Governance in service
  4. Worked example: checking a training dataset before it is frozen
Session 5 · Human factors, explainability and human-AI teaming

1.5

  1. Explainability: what the user needs to know
  2. Human-AI teaming in practice
  3. Responsibility, ethics and staying in practice
  4. From the investigation file: the pilot-response assumptions behind MCAS
Session 6 · Supply chain, data sharing and what is coming

1.6

  1. Who builds the tools
  2. Sharing data between manufacturers, airlines and MROs
  3. What is coming, with dates
  4. Worked example: testing a supplier's model on your own fleet

Who this course is for

Upskill courses are for professional development and are not approved by any aviation authority. Learner Terms

Course content © Aerospace Garage Limited. All rights reserved.