This course is built around seven GenAI competencies. Each lesson targets two or three of them, and every artifact you make is evidence of growth in at least one. Keep this page open as a reference; the objectives in each lesson use these names.
| Competency | What it means in an arts classroom | Where you practice it |
|---|---|---|
| Computational thinking | Breaking a creative task into steps, spotting patterns (verse, chorus, beat, scene, cue), and sequencing them so a tool can act on them | Lessons 1.2, 2.2 |
| Prompt engineering | Writing instructions that steer a GenAI model toward your artistic vision: genre, mood, tempo, character, setting, constraints | Lessons 1.1, 1.3, 2.1 |
| Problem solving | Diagnosing why an output missed the mark and choosing a fix: revise the prompt, change the tool, or edit the output by hand | Lessons 1.3, 2.3 |
| Creativity and innovation | Blending AI output with your own musicianship or stagecraft to produce something neither could make alone | Lessons 1.1, 2.1, 2.3 |
| AI feedback evaluation | Judging the quality, accuracy, and relevance of what a model produces or suggests, and knowing when to reject it | Lessons 1.3, 2.4 |
| AI ethics literacy | Understanding ownership, consent, bias, disclosure, and the labor behind training data, and acting responsibly | Lessons 1.4, 2.4 |
| Collaboration | Co-creating and giving feedback in shared digital spaces, including with peers and with the model itself | Every gallery and forum |
The competencies draw on three sources you may want to cite in your own work:
When you design the final project in Module 3, you will name the competency each activity builds. Try this habit now: after each lesson, ask yourself "Which competency did this artifact exercise, and how would I know a student had grown in it?"
Before moving on, rate yourself from 1 (new to this) to 5 (could teach it) on each competency in the Pre-Course Self-Assessment. You will rate yourself again at the end of the course.