Pedagogy of the Prompt: Music Education, Artificial Intelligence, and Big Tech Magic
Bibliographic record
Abstract
In this article I examine the generative AI music-making applications of Google called MusicFX DJ and Music AI Sandbox, as well as Udio, an end-to-end generative AI music system created by ex-Google employees. I frame these generative AI music systems designed by Big Tech as part of their neoliberal agenda, which involves influencing music education. Drawing on critical media theorists, I suggest that Big Tech, and in the context of this article, Google, present their generative AI music systems as “magic.” I describe how these systems work by examining how algorithmic systems, such as search engines, operate more generally, and then, more specifically, how generative AI music systems function. I aim to make legible the “magic” of generative AI music systems and explain that Google’s music-making tools, by design, appear to be agenda-less, but music educators should be wary of this illusion. I forward that Google’s “pedagogy of the prompt” may not be explicit to users of their AI music-making applications, but nevertheless it is embedded into them because they are algorithmic systems, and more specifically, generative AI/machine learning-based systems. I suggest that in this present period in which music teachers and learners are using generative AI music systems, knowing how a musical output results from an input, musical or otherwise (e.g., text), is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".