Human-in-the-Loop Engineering Systems

Course Description:

This course provides a comprehensive professional-practice examination of human-in-the-loop (HITL) engineering systems. It focuses on how engineers define and enforce decision boundaries between humans and AI, allocate control authority, design override and escalation mechanisms, and map responsibility in safety-critical, regulated, and ethically sensitive environments.
Rather than emphasizing software development, algorithms, or interface programming, the course centers on engineering judgment, ethics, accountability, governance, and audit readiness. Participants explore how HITL principles apply across engineering disciplines and why deliberate human oversight is essential for protecting public safety, maintaining system reliability, and preserving professional responsibility as AI adoption accelerates.

Learning Objectives:

Upon successful completion of this course, the participant should be able to:

1. Explain the purpose and importance of human-in-the-loop (HITL) system design in modern engineering environments that incorporate artificial intelligence and advanced automation.
2. Differentiate between traditional automation, AI-enabled decision-support systems, and autonomous systems, and describe the implications of each for professional engineering responsibility.
3. Define appropriate human–AI decision boundaries based on risk, consequence, reversibility, uncertainty, and regulatory considerations.
4. Evaluate and allocate control authority between humans and AI systems in safety-critical and regulated engineering applications.
5. Design and assess override mechanisms and escalation pathways that preserve human authority and ensure defensible decision-making under normal and abnormal conditions.
6. Map responsibility and accountability across the lifecycle of AI-assisted engineering systems, including design, deployment, operation, and governance.
7. Identify ethical risks associated with AI-assisted engineering systems, including automation bias, overreliance on algorithmic recommendations, and erosion of professional judgment.
8. Apply ethical safeguards and engineering judgment strategies to mitigate automation bias and support conservative, safety-oriented decision-making.
9. Assess documentation and governance requirements necessary to support transparency, audit readiness, and regulatory defensibility in human-in-the-loop systems.
10. Apply human-in-the-loop principles to real-world engineering scenarios, including AI-assisted operations centers and AI-supported engineering review and approval workflows.