Systems Engineering and Responsible Artificial Intelligence for Aerospace Applications – Online Short Course (Starts 24 March 2026) 24 March 2026 - 16 April 2026 Online

Class 1 (2 hours)

Introduction to Systems Engineering and AI-Intensive Systems

• Topics:
  • Overview of systems engineering as a framework for discussion
  • Systems Engineering Perspectives of AI
  • Introduction of use cases
    • 1) Collaborative Combat Aircraft
    • 2) Collaborative Autonomous Swarms
• Reading and Discussion: Grosvenor, A., Zemlyansky, A., Wahab, A., Bohachov, K., Dogan, A., & Deighan, D. (2025, July 17). Hybrid intelligence systems for reliable automation: Advancing knowledge work and autonomous operations with scalable AI architectures. Frontiers in Robotics and AI, 12.
• https://doi.org/10.3389/frobt.2025.1566623

Class 2 (2 hours)

Requirements Engineering and Risk for AI-Intensive Systems

• Topics:
  • Eliciting and specifying requirements for AI-Intensive systems
  • Risk identification, assessment, and mitigation strategies for AI systems
  • Use Case Illustration of Requirements and Risk Analysis
• Reading and Discussion: Habiba, Ue., Haug, M., Bogner, J. et al. How mature is requirements engineering for AI-based systems? A systematic mapping study on practices, challenges, and future research directions. Requirements Eng 29, 567–600 (2024).
• https://doi.org/10.1007/s00766-024-00432-3

Class 3 (2 hours)

Fielding the AI Intensive System

• Topics:
  • System Architectures
  • AI Component Definition and Integration
  • Testing, Verification, and Validation
• Reading and Discussion: Christensen, J. M., Stefani, T., Anilkumar Girija, A., Hoemann, E., Vogt, A., Werbilo, V., Durak, U., Köster, F., Krüger, T., & Hallerbach, S. (2025).Formulating an Engineering Framework for Future AI Certification in Aviation. Aerospace, 12(6), 482.
• https://doi.org/10.3390/aerospace12060482

Class 4 (2 hours)

How AI is Changing Aerospace Engineering

• Topics:
  • Part design
  • Inspection processes,
  • Route optimization and air traffic management
• Reading and Discussion: Zhuoming Du, Jiaxuan Wu, Yuanfei Leng, Sebastian Wandelt, AI4ATM: A review on how Artificial Intelligence paves the way towards autonomous Air Traffic Management, Journal of the Air Transport Research Society, Volume 5, 2025,
• https://doi.org/10.1016/j.jatrs.2025.100077

Class 5-6 (4 hours)

• AI, Autonomy & Human Decision-making: AI and autonomy are defined, along with explanations of how symbolic and connectionist AI function (including generative AI). These will be compared and contrasted to human decision-making.
• Risk & Systems: Risk and systems engineering fundamentals will be reviewed, with a discussion about how AI has changed these approaches.
• Hazards & Risks: Hazards unique to AI in aerospace systems will be covered as well as how hazard analysis techniques should be updated.
• Uncertainty & Perceived Risk: How, where and why uncertainty influences AI-embedded systems will be discussed, as well as the risk that various sources of uncertainty are introduced into aerospace systems.
• AI & Safety: How safety models have changed with the introduction AI in aerospace applications will be discussed, along with AI safety management techniques.
• Human-AI Interaction: This module will discuss to pros and cons of human interactions with AI in both the design and use of aerospace systems.
• Explainable/Interpretable/Trustworthy AI: Definitions will be discussed, along with AI use cases to illustrate how these different concepts can be applied in aerospace applications.
• Testing & AI: How and why AI affects both developmental and operational testing will be discussed, along with unique AI considerations like software upgrades and certification concerns.
• AI Bias Management: How to manage the bias that is inherent in all AI systems will be discussed across the aerospace systems engineering lifecycle.
• AI, Cybersecurity & Risk: This module will cover what unique changes AI brings to aerospace cybersecurity and current mitigation strategies.
• Reading and Discussion: National Academies of Sciences, Engineering, and Medicine. (2025). Machine learning for safety-critical applications: Opportunities, challenges, and a research agenda. Washington, DC: The National Academies Press.
• https://doi.org/10.17226/27970

Class 7-8 (4 hours)

• Introduction—Applied Ethics and AI: review and examination of how values and principles can be used to guide the development and application of AI systems for societal benefit.
• Responsible Innovation and Responsible AI (RAI): focuses on fostering innovation while ensuring that AI technologies are ethically sound, socially beneficial, and align with human values.
• RAI Principles and Frameworks: learn how RAI principles and frameworks provide a foundation for the ethical design, development, and governance of AI systems across industries.
• RAI Principles, Frameworks, and Guidelines: Explore guidelines based on RAI principles and frameworks and how they help organizations create AI systems that prioritize ethical considerations, accountability, and societal good.
• RAI Principles, Frameworks, and Guidelines Case Study: Review and analyze an aerospace case study, focusing on the use of AI in aircraft design and safety, and how RAI principles frameworks and guidelines intersect with key features of the case.
• Operationalizing RAI: Methods and Tools: learn how practical methods and tools are used to integrate RAI into the everyday design, development, and deployment of AI systems in aerospace.
• Fairness: this session explores how fairness plays a key role in ensuring that algorithms are designed to produce equitable outcomes for all individuals and groups, avoiding bias and discrimination.
• Transparency and Trust: explore how transparency in AI can foster trust in aerospace by making the decision-making processes of AI systems understandable and accessible to users and stakeholders.
• Accountability: this session focuses on holding developers, organizations, and systems responsible for the outcomes of AI decisions and ensuring mechanisms for redress.
• Safety and Security: this section focuses on preventing harm from AI systems by ensuring they operate reliably, securely, and with proper safeguards.

COURSE DELIVERY AND MATERIALS

• The course lectures will be delivered via Zoom. Access to the Zoom classroom will be provided to registrants near to the course start date.
• All sessions will be available on demand within 1-2 days of the lecture. Once available, you can stream the replay video anytime, 24/7.
• All slides will be available for download after each lecture. No part of these materials may be reproduced, distributed, or transmitted, unless for course participants. All rights reserved.
• Between lectures during the course, the instructor(s) will be available via email for technical questions and comments.

Dr. Phil Barry is a strategic and technical leader with a robust background in systems engineering, military simulation, and space mission-critical systems. As the Director of the Mission and Systems Solutions Group at the L3Harris Corporation, he leads future architecture definition, advanced concept refinement, and modeling, simulation, and analysis projects to deliver key capabilities for the DoD and the Intelligence Community. Dr. Barry is also an Adjunct Professor at George Mason University where he teaches courses in the Systems Engineering of Artificial Intelligence, Heterogeneous Data Fusion, Decision Analysis, and Project Management. He has a Ph.D. in Information Technology, an MS in Systems Engineering, and a BS in Aerospace Engineering.