Course Overview

Artificial intelligence is becoming increasingly relevant across the aviation sector, supporting everything from maintenance and operations to air traffic management and unmanned systems. This course provides a comprehensive introduction to the concepts, regulatory framework, and guidance material shaping the safe and trustworthy use of Artificial Intelligence (AI) in aviation, specifically aligned with EASA’s requirements.

The curriculum comprehensively covers key AI definitions, the current aviation regulatory landscape, and the four pillars of EASA’s trustworthy AI framework: Trustworthiness Analysis, AI Assurance, Human-Centred Design, and Safety Risk Mitigation. Learners will gain a foundational understanding of how to introduce, supervise, and regulate AI applications safely, responsibly, and in accordance with emerging European expectations.

Target Audience

“Trustworthy AI in Aviation” course has been designed for a broad aviation audience. It is highly recommended for:

• Organisations and individuals introducing AI/ML technologies to aviation use cases.
• End users like flight crews, ATCOs, or maintenance staff who need to understand AI outputs.
• Professionals involved in developing, supervising, evaluating, approving, or regulating AI technologies, including pilots, engineers, technicians, air traffic controllers, aviation psychologists, safety specialists, managers, and authority personnel.

Note: No previous experience in artificial intelligence is required. The emphasis is on understanding principles, concepts and practical implications rather than programming, mathematics or data science.

Recommended Preparation: If you want to build a foundational understanding of AI technologies before diving into the EASA regulatory framework, we highly recommend reviewing our free course: Artificial Intelligence for Aviation Applications.

Learning Outcomes

By the end of the course, learners will be able to:

Knowledge

• Explain the purpose and interaction of the four building blocks of EASA’s AI trustworthiness framework: Trustworthiness Analysis, AI Assurance, Human Factors for AI, and Safety Risk Mitigation. 
• Describe the relationship between the EU AI Act and EASA’s aviation-specific framework for the safe and trustworthy use of AI in aviation. 
• Identify EASA’s AI application classification levels, including Level 0, Level 1, Level 2 and Level 3, and explain how these levels relate to assistance, teaming, low automation and advanced automation. 
• Explain how authority is distributed between humans and AI systems, including the concepts of directed, supervised, safeguarded and non-supervised authority, and their relevance to AI classification. 
• Describe Human-AI Teaming concepts, including cooperation, collaboration, shared authority, shared situation awareness and their relationship to Level 2 AI applications. 
• Describe key concepts introduced by EASA for trustworthy AI in aviation, including Learning Assurance, Operational Domain, Operational Design Domain, Data Quality Requirements, development and post-operational explainability, operational explainability, data recording and AI Assurance Levels. 
• Discuss the AI application lifecycle, including the transition from traditional programming to data-driven learning, the W-shaped learning assurance process, and the broader lifecycle considerations introduced for AI-based systems. 
• Recognise the expanded technical scope introduced in Proposed Issue 3, including reinforcement learning, logic- and knowledge-based AI, symbolic AI, hybrid AI and advanced automation.

Skills

• Use EASA’s four-building-block framework to analyse the trustworthiness considerations relevant to an AI-enabled aviation application. 
• Interpret the main stages of the W-shaped learning assurance process and explain how they support the development, verification and integration of AI or machine-learning constituents. 
• Identify Operational Domains and Operational Design Domains relevant to an AI application and explain how they influence data selection, model development, verification and operational monitoring. 
• Identify Data Quality Requirements relevant to AI applications, including accuracy or correctness, completeness, representativeness, independence and the management of unwanted bias. 
• Classify AI-enabled aviation applications using EASA’s AI levels and authority concepts, including Level 0, Level 1, Level 2 and Level 3 applications. 
• Evaluate Human-AI Teaming interactions at a conceptual level, including risks such as automation bias, over-reliance, loss of situation awareness and de-skilling. 
• Apply the principles of EASA’s ethics-based assessment framework using the seven Gears of Trustworthy AI adapted from the EC Ethics Guidelines and ALTAI. 
• Identify potential aviation use cases for different AI techniques, including machine learning, reinforcement learning, symbolic AI, hybrid AI and advanced automation concepts. 
• Propose appropriate safety risk mitigation considerations for AI-related hazards, residual risks, operational monitoring limitations and advanced automation scenarios. 
• Interpret EASA guidance material relevant to AI-enabled aviation systems, including the AI Roadmap, Concept Paper Issue 2, Proposed Issue 3, MLEAP outputs, RMT.0742 material and NPA 2025-07.

Competency

• Contribute effectively to the evaluation, deployment, oversight, assurance or operational use of AI-enabled aviation systems within the context of EASA’s evolving trustworthiness framework.

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