Teacher Guidelines
Overview
| Element | Implementation | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Time | 2–3 lessons + homework | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Quality band | B (practice) or C (full project with submission) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Group size | Individual project; listening and discussion in groups | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Tools | AI tool with web search; a free account is enough | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| (Fill-in template table) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Why This Theme Works
Music is relatable to the student and loaded with emotion; this makes the human anchor strong and personal. The theme is particularly suitable for students who live in the streaming world (see Dagur's user story). Each step is short and defined, which suits students who struggle with long, dense projects.
Preparation
- No special tools beyond a single AI tool with web search.
- You can start the lesson by playing two songs — one from ~1995, one from ~2024 — and let students guess the difference before the machine analyzes it.
- Read the completed example for the expected quality standard.
Progression
- Step 1 (15–20 min, without machine): students write about their music. No devices.
- Step 2 (lesson): the ICCRA prompt; emphasis on separating data from interpretation.
- Step 3 (lesson + homework): Honest Oracle, "what is lost/gained", judgment.
- Discussion: played examples; who found a counter-example that broke the pattern?
Common Pitfalls
- Opinion taken as data. "Songs are worse now" is an interpretation, not a measurement. Demand differentiation.
- One-sidedness. Some students only see loss. Demand both columns in Step 3 — algorithms also made new creation possible.
- Hallucinated research. Machines invent "Spotify studies" with numbers. Demand fact-checking.
- Echo chamber. A student who only asked the machine to confirm a preconceived opinion got an echo, not an analysis.
Adaptation
- Creative subjects/music studies: delve into mixing, arrangement, release tactics.
- Academic studies: connect to the attention economy and the dopamine loop as economic and psychological phenomena.
- Shorter version: skip the period comparison, focus on one song and one hook.
Discussion Questions
- Who writes the song, the artist or the algorithm that rewards a certain format?
- Is a shorter song a worse song, or just a different song?
- What is the difference between creating for a human and optimizing for skip-rate?_