Our Approach
How we develop and refine Symbiotic Thinking through continuous iteration
A Different Starting Question
Many efforts to integrate AI into education ask: How do we add AI to what we already do?
We start with a different question:
When AI changes what work looks like, what should learning become?
If execution can be delegated...
Does human value shift from knowing how to do things toward knowing whether something was done well and what purpose it serves?
If AI handles the doing...
How do you develop judgment without the traditional runway of hands-on experience? What new pathways to expertise emerge?
We don't claim to have answers. This is an experiment driven by curiosity and a willingness to explore. The Dojo is designed for local experimentation — we build, measure with real students, and learn. Our approach may need fundamental rethinking, and that's exactly why we built tools that let us iterate quickly.
The Build-Measure-Learn Loop
We develop content through continuous iteration — building, testing with students, learning from results and the field, then building better.
BUILD
Develop content that meets students where they are
MEASURE
Test with real students, observe what resonates
LEARN
Synthesize insights from measurement and the field
From what we measured
Student feedback, what clicked, what confused
From the field
Researchers, practitioners, diverse perspectives
Example: Creating vs. Consuming
We initially presented "Creating vs. Consuming" as a choice between two approaches. Testing with students revealed they interpreted these as opposing options — you either create or you consume. We refined the message to emphasize they're not opposing forces, but a continuous spectrum to be balanced — the goal is to infuse the act of consuming with the habits of creating. This insight came from measuring, and it changed how we build.
Testing in Practice
Symbiotic Thinking is the core of these efforts. Each program builds additional layers of concepts and skills for different audiences — giving us opportunities to measure and learn.
CST395: AI-Native Solution Engineering
A special topics course where students learn to solve authentic problems using AI as a cognitive partner. Building on Symbiotic Thinking, the course develops the capabilities required for Human Agency in the age of AI:
🧭Self-Directed LearnersBuild a meta-learning architecture—knowing what to learn versus what to strategically ignore, using AI as learning infrastructure for just-in-time depth.
Build a meta-learning architecture—knowing what to learn versus what to strategically ignore, using AI as learning infrastructure for just-in-time depth.
Example: When building patient communication tools, rapidly acquire expertise in medical terminology, HIPAA regulations, clinical workflows, and patient psychology—in days, not semesters.
🔗Integrative SolversDevelop T-shaped expertise and operate at intersections between systems where human value concentrates. Understand the forces and incentives operating in a space.
Develop T-shaped expertise and operate at intersections between systems where human value concentrates. Understand the forces and incentives operating in a space.
Example: When analyzing customer complaints, bridge from technical capability to human workflow—discovering that simple frequency analysis creates more value than a sophisticated sentiment dashboard.
🔄Adaptive BuildersExecute through cycles of building, testing, learning from failures, and adapting. Exercise restraint in complexity—simple solutions executed well often deliver more value.
Execute through cycles of building, testing, learning from failures, and adapting. Exercise restraint in complexity—simple solutions executed well often deliver more value.
Example: Start with basic frequency analysis rather than ambitious sentiment analysis, testing whether it solves the core problem. Iterate toward value, not complexity.
Applying AI at Work: Build Solutions with a Human Touch
A two-course certificate program helping working professionals develop human-AI collaboration skills. Building on Symbiotic Thinking, the program focuses on:
Learn to identify the real problem worth solving before jumping to solutions—a critical skill when AI can generate solutions faster than humans can evaluate them.
Develop judgment about where human intervention adds value and where AI can be trusted to handle tasks independently.
Move from concept to implementation, learning to iterate quickly and validate solutions with real stakeholders.
Sources That Inform Our Thinking
The AI landscape changes rapidly. These books, researchers, podcasts, and papers represent diverse perspectives — some optimistic, others cautionary. Exploring both helps form informed views.
Remember: The goal isn't to read everything, but to develop informed perspectives by exploring diverse viewpoints. Pick resources that challenge your current conceptions and respond to your current questions about AI.
See the framework in action
Explore the three layers of Symbiotic Thinking and try the Dojo for yourself.