Problem Identification & AI Foundations for Safety-Critical Environments

Elevating Aerospace: Design Thinking for Safety Critical AI is a hands-on, applied course for aerospace professionals seeking practical, responsible, and deployment-oriented AI capabilities in regulated and mission-critical environments. Rather than focusing on theory alone, the course engages students in active experimentation with real operational problems, realistic aerospace workflows, and current AI tools and platforms.

Students begin with structured problem discovery, identifying operational inefficiencies, risk points, workflow constraints, and opportunities for AI-enabled improvement across engineering, manufacturing, quality, supply chain, program management, and mission-support contexts. Through guided labs and applied exercises, students use AI tools to analyze documents, summarize technical information, map workflows, generate structured outputs, test prompts, compare tool capabilities, and prototype AI-assisted processes using non-sensitive or appropriately handled data.

The course builds foundational literacy in machine learning, deep learning, generative AI, agentic systems, computer vision, and large language models, with emphasis on realistic capabilities, limitations, and failure modes. Students apply design thinking as a framework for safety-critical AI adoption, framing solutions around human judgment, transparency, trust calibration, cognitive load, auditability, and operational fit.

The course also examines the distinction between prescriptive and descriptive AI, helping students evaluate when AI should recommend action and when it should provide structured insight for human decision-making. Students then translate AI concepts into supervised workflows with defined inputs, outputs, triggers, review checkpoints, escalation logic, and traceability.

As the course progresses, students experiment with AI platforms and tool-stacking approaches while exploring secure AI architecture, cloud versus local/on-device deployment trade-offs, data classification, access controls, monitoring, logging, and governance safeguards. The course culminates in a capstone-style deliverable in which each student designs and presents a pilot-ready AI system concept for a real aerospace use case, including workflow architecture, deployment strategy, human-centered interaction model, risk controls, and an executive-ready implementation narrative.