Teaching philosophy
Teaching intelligent systems without teaching students to surrender judgment.
This page is intentionally structured as an editable philosophy workshop. Replace the bracketed guidance with your final 700–1,000 word statement and supporting evidence.
Editing note: Keep the philosophy personal and evidence-based. Use one or two concrete teaching moments rather than a list of educational terms. Link each claim to something students actually experience.
1. Why I teach
[Begin with the change you want students to experience. Consider the moment in which students become absorbed in making an AI system work—and your desire to help them ask what responsibilities accompany that capability.]
2. How I believe people learn
[Explain why building, testing, explaining, revising, and reflecting are central to learning complex technical ideas.]
3. What students experience in my courses
[Describe project-based AI work, hands-on learning, interactive notebooks, sensor systems, scenario analysis, active discussion, and transparent assignment design. Add one vivid example.]
4. Rigor, inclusion, and relationships
[Explain how you combine high expectations with clear structures, multiple entry points, meaningful feedback, and relationship-rich learning.]
5. Assessment and continuous improvement
[Explain what counts as evidence of learning: reasoning, transfer, explanation, model evaluation, project quality, reflection, or peer learning.]
6. Teaching in an AI-integrated world
[Conclude with your responsibility as an AI educator. Students need technical fluency and judgment: the ability to question outputs, recognize uncertainty, evaluate consequences, and remain accountable.]
Add philosophy portrait
A reflective, relational, or classroom-centered photograph.
Evidence to link
- AI Systems project
- CNN modular learning experience
- Faculty AI Fundamentals module
- AHA! curriculum
- Student feedback or peer observation