Frame the system
Draw a useful boundary, identify stakeholders, and trace feedback rather than analyzing isolated parts.
Field guide 01 / Systems engineering
Systems Engineering Foundations
Learn to see relationships, define boundaries, manage interfaces, and connect engineering evidence—one illustrated lesson at a time.
Reading order
Start with the whole. Then add complexity.
Each lesson stands alone, but the sequence builds a useful vocabulary before adding new kinds of complexity.
- 01Systems Thinking
- 02Modular Design Systems
- 03Model-Based Systems Engineering
- 04Systems of Systems Engineering
- 05Digital Twins
- 06AI-Enabled Systems Engineering
- 07Requirements Gathering and Engineering
- 08Systems Architecture
- 09Foundations of Human-Automation Interaction
- 10Trust, Complacency, and Situation Awareness
- 11AI Teammates: What's Actually New
- 12History and Future of Autonomous Systems
The course
12 illustrated lessons
01
Systems Thinking
Move beyond isolated events to understand boundaries, accumulation, feedback, delay, and emergence.
02Modular Design Systems
Decompose systems around responsibility, hidden decisions, and explicit interface contracts.
03Model-Based Systems Engineering
Make a connected model, not a document stack, the primary engineering artifact—SysML, the v2 standards shift, and an honest look at the evidence.
04Systems of Systems Engineering
When the thing you engineer is made of independently owned systems: Maier's five characteristics, the four types, emergence as the core challenge, and the multi-agent AI parallel.
05Digital Twins
A live virtual model of a physical system, where twins attach to the Vee, why they need continuous V&V, and what changes when AI is added.
06AI-Enabled Systems Engineering
What happens when an AI agent reads and writes the system model directly—where the MBSE-plus-agent partnership is real today, and where it is still a research proposal.
07Requirements Gathering and Engineering
The requirements hierarchy, what makes a statement well-formed, EARS syntax and its five patterns, an honest look at the CHAOS-report evidence, and how EARS now constrains AI-generated code.
08Systems Architecture
Not the diagrams but the decisions underneath them: viewpoints and views, Kruchten’s 4+1, architecting as a heuristic discipline, and what a weighted trade study actually proves.
09Foundations of Human-Automation Interaction
Part 1 of the Human Systems Integration primer: function allocation and the Fitts list, the evidence that reversed it, ten levels of automation, the four-function model, and the ironies of automation.
10Trust, Complacency, and Situation Awareness
Part 2 of the Human Systems Integration primer: Endsley’s three levels, the out-of-the-loop problem, use/misuse/disuse/abuse, calibrated trust, and why training alone does not prevent automation bias.
11AI Teammates: What's Actually New
Part 3 of the Human Systems Integration primer: silent model updates, goal delegation instead of function allocation, failures that sound right, and how many agents one person can oversee.
12History and Future of Autonomous Systems
A special feature spanning both primers: an interactive timeline from Watt’s governor and cybernetics through Shakey, two AI winters, the DARPA challenges, and the transformer to agentic AI.
Working skills
What you will practice
Design for change
Use cohesion, coupling, interfaces, and event-driven communication to make modular tradeoffs explicit.
Connect evidence
Relate needs, requirements, architecture, analysis, and verification through traceability and viewpoints.
Coordinate autonomy
Reason about governance, interoperability, emergence, and cascading risk across independent systems.
Lesson format
Read it. See it. Trace the sources.
- LearnBeginner-focused explanations use cited standards, handbooks, and foundational literature.
- SeeOriginal technical line-art diagrams make each concept and relationship visible.
- VerifyEvery claim carries an in-text citation, with full APA references per lesson.
- KeepEach lesson prints to a clean, ink-friendly PDF for offline study.
On the syllabus
Notes for future lessons
These topics are in progress. They will be published as the examples, exercises, and references are ready.
Verification and Validation
Build evidence that the system was built correctly and fulfills its intended use.
Trade Studies and Decision Analysis
Compare alternatives transparently under competing objectives and uncertainty.
Risk and Opportunity Management
Identify, analyze, treat, and monitor uncertainty across the life cycle.
Interface Management
Define, control, verify, and evolve cross-boundary agreements.
Digital Engineering
Connect authoritative data, models, workflows, and evidence across engineering work.
System Life-Cycle Processes
Tailor technical and management processes to the system, organization, and life-cycle model.
Human Systems Integration
Design human roles, work, training, safety, and technology as one system.
Reliability and Resilience
Reason about failure, recovery, adaptation, and sustained capability.
Configuration Management
Keep product definitions, baselines, changes, and evidence coherent over time.
Technical Measurement
Use measures and indicators to understand progress, performance, and uncertainty.
Systems Engineering for AI-Enabled Systems
Engineer data, models, human oversight, change, and assurance as an integrated capability.