I've spent 12+ years in classrooms and over two decades building AI systems. The overlap is bigger than you'd think.
Both humans and machines learn through iteration, feedback, and carefully structured experiences. The principles transfer.
What Teaching Taught Me About AI
1. Scaffolding Matters
Good instruction builds from foundations. You don't teach calculus before algebra.
In AI systems, this translates to:
- Progressive complexity in training data
- Curriculum learning that starts simple
- Prerequisite encoding in knowledge graphs
The best RAG systems I've built have structured knowledge hierarchies—they "learn" the basics before tackling edge cases.
2. Feedback Is Everything
In teaching, immediate feedback accelerates learning. Students who get quick responses on their work improve faster.
For AI:
- RLHF (Reinforcement Learning from Human Feedback) works because it's feedback
- Quality loops that capture thumbs up/down improve over time
- Error analysis is just feedback at the system level
When we built our enterprise AI platform, we instrumented feedback everywhere. Every user interaction is a training signal.
3. Context Unlocks Understanding
Students learn better when they understand why something matters. Context creates motivation and retention.
For AI systems:
- Contextualized prompts outperform bare queries
- Chain-of-thought reasoning shows the work
- Retrieval-augmented systems provide grounding
The parallel is direct: both learners need context to make sense of new information.
What AI Taught Me About Teaching
1. Personalization Scales
AI can adapt to individual learners in ways human instructors can't:
- Pacing adjusts to comprehension
- Examples match background knowledge
- Difficulty calibrates to performance
This has changed how I design courses. I create modular content that can be assembled based on student needs.
2. Explanation Is Hard
Making LLMs explain their reasoning has humbled me. It's genuinely difficult to articulate why a conclusion follows from premises.
This has made me more patient with students who struggle to explain their understanding. Knowing and articulating are different skills.
3. Failure Modes Are Informative
AI systems fail in predictable patterns:
- Overconfidence in familiar territory
- Confusion at boundary cases
- Hallucination when knowledge is thin
Humans have similar patterns. Recognizing them makes me a better teacher.
The Meta-Lesson
Both teaching and AI development are fundamentally about:
- Curating experiences that lead to understanding
- Creating feedback loops that drive improvement
- Building scaffolds that support complexity
- Accepting iteration as the path to quality
The 5,000+ students I've taught and the AI systems I've built are all part of the same project: understanding how learning works and how to make it work better.
Teaching made me a better AI architect. AI made me a better teacher.
The disciplines aren't separate—they're two views of the same problem.