Pedagogy of the Prompt: Music Education, Artificial Intelligence, and Big Tech Magic.

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Title: Pedagogy of the Prompt: Music Education, Artificial Intelligence, and Big Tech Magic.
Authors: Bell, Adam Patrick1
Source: Action, Criticism & Theory for Music Education. Jun2025, Vol. 24 Issue 3, p202-244. 44p.
Subject Terms: *Music education, *Artificial intelligence, *Algorithms, *Music teachers
Reviews & Products: Google (Web resource)
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. [ABSTRACT FROM AUTHOR]
Copyright of Action, Criticism & Theory for Music Education is the property of Mayday Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Action, Criticism & Theory for Music Education is the property of Mayday Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Algorithms
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              Text: Jun2025
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