What you're developing throughout the course
CST395 is designed around three core capabilities that together enable you to create value in an AI-augmented world. These aren't separate skills—they work together. Every sprint activity develops multiple capabilities, with different emphases at different stages of your journey.
Developing a meta-learning architecture for rapid expertise
Self-Directed Learners develop a meta-learning architecture—a systematic approach to acquiring expertise in unfamiliar domains rapidly. This capability requires more than desire to learn; it's knowing what you need to learn versus what you can strategically ignore, and using AI as learning infrastructure to build the required just-in-time depth.
When a student starts work on building patient communication tools, they need to understand medical terminology, HIPAA privacy regulations, clinical workflows, patient psychology under stress, and how different age groups interact with technology—in days or weeks, not semesters.
Sprint 1: Learn about yourself and your own challenges
Sprint 2: Learn about someone else's domain
Sprint 3: Learn from limited information and indirect sources
Sprint 4: Independently identify what you need to learn
Operating at intersections where human value concentrates
Integrative Solvers apply self-directed learning capacity across domains, developing T-shaped (or even m-shaped) expertise—deep knowledge in one technical area combined with broad boundary-crossing competencies. In the AI age, the horizontal bar becomes critical: they operate at intersections between systems of knowledge where AI struggles but human value concentrates.
When analyzing customer complaints, an Integrative Solver doesn't just build the requested sentiment analysis dashboard. They spend time with the complaint handlers, discovering that 50 patients generate 60% of complaints—but nobody realized this because complaints were filed by date, not patient. They bridge from technical capability to human workflow, delivering simple frequency analysis that creates more value than a sophisticated tool.
Sprint 1: Understanding yourself as stakeholder
Sprint 2: Deep engagement with someone you know—bridging to their domain
Sprint 3: Building understanding without easy access—connecting indirect sources
Sprint 4: Independently integrating across multiple stakeholder domains
Executing through cycles of building, testing, and adapting
Adaptive Builders synthesize self-directed learning and integrative solving to execute, developing through cycles of building, testing, learning from failures, and adapting. They exercise restraint in complexity, discovering through iteration that simple solutions executed well often deliver more value than sophisticated ones.
An Adaptive Builder starts with basic frequency analysis rather than ambitious sentiment analysis, testing whether it solves the core problem. Through iteration, they learn to meet user needs without over-engineering. The cycle teaches them to distinguish symptoms from root causes and to iterate toward value rather than complexity.
Sprint 1: Build with high guidance—learn the iteration cycle
Sprint 2: Build for someone else—adapt based on their feedback
Sprint 3: Build with constraints—iterate with limited information
Sprint 4: Build with full autonomy—own your iteration process
Every sprint ends with a Human Value Statement where you articulate what YOU specifically contributed that AI couldn't. This isn't about competing with AI—it's about understanding where human judgment, taste, and direction are essential. Examples include:
These capabilities aren't separate—they're integrated: