AI Concepts to Structured Execution — From Insight to Workflow Design

This course moves students from AI awareness and problem framing into structured execution. Building on the problem statements and AI foundations developed in Course 1, students learn how to translate aerospace challenges into practical AI-enabled workflows that can operate within safety-critical and regulated environments.

Students work with realistic aerospace scenarios and experiment with AI tools and platforms to decompose problems into tasks, define workflow steps, test prompt structures, generate structured outputs, and evaluate how AI can support analysis, reporting, routing, monitoring, and decision support. The emphasis is on applied workflow design rather than abstract theory.

The course introduces AI systems thinking, including system boundaries, failure modes, guardrails, fallback logic, audit trails, role-based access considerations, and traceability. Students learn to define what information enters the workflow, how it is processed, where human review is required, what outputs are produced, and how exceptions or uncertain results should be escalated.

Students also apply the prescriptive versus descriptive AI framework at the workflow level. They examine where AI should provide recommendations, where it should remain informational, and how these choices affect cognitive load, trust, safety, and accountability. UI/UX considerations are introduced to help students design interactions that support transparency, explainability, and effective human oversight.

Hands-on labs may include engineering change routing, quality report generation, requirements-to-test mapping, supply chain monitoring, documentation analysis, and operational performance summaries. By the end of the course, students produce a documented workflow architecture aligned to a real aerospace problem, including human-in-the-loop checkpoints, risk controls, and implementation considerations.