Breaking the Boundaries: Philosophical Encounters with Artificial Intelligence in Music Education
Bibliographic record
Abstract
Artificial Intelligence (AI) technology is reshaping the ways humans study and work across various disciplines. In the field of music, AI technology shows its possibility to empower individuals at diverse levels of musical knowledge in music creation, from novices to experts. In this article, I explore philosophical questions within both AI theories and music education, and specifically demonstrate two empirical instances of humans’ musical interactions with Generative AI technology. I argue that music education is facing an uncertain yet promising future at the confluence of AI theories and practical applications in music learning. Music educators must engage in deeper sociological and philosophical reflection on their pedagogical practices, integrating with AI technology and its implications to music learning with critical humility to consider various access possibilities to (potential) learners, to foster richer interactions between humans and computers, and to transcend existing teaching practices for sustainable and inclusive music practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.139 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".