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REVIEW 3 major objections 6 minor 82 references

Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Co-design with 13 practising musicians shows AI music tools should be tools, not collaborators

desk verdict A solid co-design case study with a couple of soft spots: the framing insight is overreach and a quote looks misattributed. read the letter →

arxiv 2502.09055 v1 pith:RYKCIXF3 submitted 2025-02-13 cs.HC cs.AI

Stephen James Krol , Maria Teresa Llano Rodriguez , Miguel Loor Paredes This is my paper · ORCID
classification cs.HCcs.AI
keywords co-designco-creativeAIgenerativemusicvariationpractisingmusicianshuman-AIinteractionecologicalevaluationBERT
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the dominant way of building co-creative AI for music—developing a generative model first and testing it on musicians only at the end—skips the very people who will use it. Through two co-design workshops and a two-week ecological evaluation with 13 practising musicians, the authors developed a musical variation tool and derived design insights for co-creative musical AI. The strongest of these insights is that how the technology is framed, as a collaborator or as a tool, affects whether practising musicians will adopt it, and that musicians across backgrounds strongly want to retain ownership of the creative process. A sympathetic reader would take the central claim to be that co-design with practising musicians from the outset reveals needs, such as ideation support with controllable variation, that are otherwise easy to miss.

What carries the argument

The carrying mechanism is the co-design methodology itself—two workshops that let musicians compose with AI probes and specify features, followed by a two-week ecological evaluation inside the musicians' own environments—combined with a generative model whose controllability matches the co-design requirements. The technical core is MusicBERT with Octuple encoding, which represents each note by eight attributes (bar, instrument, position, pitch, duration, velocity, time signature, tempo). The authors extend a fork of MidiFormers so users can mask and re-predict these attributes at bar level, select which bars to vary, and add new notes whose attributes the model predicts. This combination gives the musician fine-grained control over what is varied, which is exactly what the workshops identified as necessary.

What would settle it

A controlled comparison with a larger and more diverse sample, for instance professional musicians over 45 and performers who do no formal composition, testing whether the same preference for 'tool' framing over 'collaborator' framing and the same feature priorities emerge, would directly test the generality of the findings. A second check would be whether a DAW-integrated release of the variation tool sees sustained use beyond a two-week compensated study.

Watch

Extended reading notes

Core claim

The paper claims that involving practising musicians as partners throughout the design process, rather than as late-stage testers, changes both what gets built and what we learn about the users. Working with 13 musicians of varied backgrounds across two workshops and a two-week 'in the wild' evaluation, the authors designed a deep-learning-based music variation tool built on MusicBERT's Octuple encoding, extended with bar-level masking, bar control, and an add-new-notes function. The study found that participants consistently rejected the notion of AI as a collaborator, defending the term 'collaborator' as reserved for humans, while welcoming the same technology when framed as a controllable tool that generates variations for ideation. This led to the paper's central design insights: preserve ownership of the creative process, bridge traditional musical terminology and machine representations, account for the musician's background, and plan for integration into the existing DAW ecosystem.

Load-bearing premise

The design insights rest on the assumption that 13 convenience-recruited musicians, mostly under 45 and drawn from a university music faculty and the researchers' networks, are sufficiently representative of practising musicians to support general design guidance.

Editorial extensions

If this is right

  • Designers of co-creative music AI should frame their systems as tools that support ideation, not as collaborators, to improve adoption among practising musicians.
  • Systems that preserve musicians' ownership of final decisions and offer adjustable levels and types of variation will fit better into existing workflows.
  • Attribute naming and interface vocabulary should be drawn from musical tradition rather than raw MIDI terminology, with adaptations for musically diverse users.
  • Co-creative systems should be planned as DAW integrations from the outset, since musicians operate within established production ecosystems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is whether the 'collaborator vs tool' framing effect holds for visual artists and writers, where professional identity and ownership concerns may play out differently.
  • The paper leaves implicit that co-design with practising musicians is costly and hard to scale; lightweight cohort methods, such as short structured feedback loops with paid consultants, may be a pragmatic alternative for fast-moving generative model development.
  • The 'add new notes' mechanism suggests a broader design pattern: letting users seed masked tokens as partial compositions is a way to make generative models steerable without retraining.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper reports a co-design case study with 13 practising musicians, using two workshops and a two-week ecological evaluation to develop a music variation tool based on MusicBERT and to derive design insights for co-creative music AI. The authors claim that co-design can identify an appropriate role for such AI early in development, and they present design insights around framing as collaborator versus tool, ownership of the creative process, terminology gaps, the influence of diverse musical backgrounds, and integration into existing ecosystems.

Significance. If the findings are treated as transferable, the work is a useful and modest contribution to HCI and co-creative AI research: it provides a concrete worked example of co-design with practising musicians, rich qualitative data tied to participant quotes, and practical design suggestions. The ecological evaluation in participants' own environments and the explicit limitations section are strengths. The main contribution is a process demonstration and candidate design insights rather than a new generative model, and those insights need more cautious framing before they can serve as general guidance.

major comments (3)
  1. [4.1 (also 3.1.4)] The design insight that 'how the framing of their system, as a collaborator or as a tool, may impact its adoption' is not directly supported by the data. The study never manipulated framing: Workshop 1 participants discussed and rejected the 'collaborator' label, while the ecological evaluation deployed only the tool-framed system. No condition presented the same underlying system under a collaborator framing, so framing is confounded with the system's interface, capabilities, and the participants' prior discussion. The evidence is an expressed attitude, not a measured adoption outcome. I recommend rephrasing this insight as a hypothesis or as a sample-specific preference, and removing the causal 'impact on adoption' claim unless a comparative condition is added.
  2. [3.4.1] The quote 'it sort of gave me something new I never really thought of before' is attributed to P7, but P7 does not appear in the ecological evaluation participant table (Table 4) and only appears in Workshop 2 (Table 2). This appears to be a misattribution or a mixing of data across studies. Because this quote is used to support the ecological finding that the system's impact was most significant in the ideation phase, the thematic analysis for this subsection needs to be re-verified and any misattributed quotes corrected or removed.
  3. [4.1-4.5 (sample and generalizability)] Section 4 presents the findings as design insights for 'future designers of co-creative musical systems for practising musicians,' but the empirical basis is 13 convenience-recruited musicians, mostly under 45, with no older musicians and no inter-rater reliability metrics or member checks. The authors acknowledge the age limitation in Section 5.3, but the general design-guidance framing in Section 4 still overstates the reach of the results. I ask the authors to either temper these claims to the studied sample or provide an explicit analytic-generalization argument explaining which insights are expected to transfer and why.
minor comments (6)
  1. [Tables 2 and 4] P8's age range is listed as '22-34' in Table 2 but '25-34' in Table 4; the two tables should be consistent.
  2. [3.3] The text says MusicBERT's Octuple encoding allows remixing across 'eight different attributes' but then lists Bar, Instrument, Position, Pitch, Velocity, Tempo, and Time Signature, which is seven attributes; Duration is missing from the list and should be added.
  3. [5.3] The DAW name 'Abelton' is a typo and should be 'Ableton'.
  4. [Throughout] The capitalization of 'MusicBert' is inconsistent with 'MusicBERT' in Section 3.3; please standardize to the official name.
  5. [3.1.2] The phrase 're-harmonisation's of their music' contains an unnecessary apostrophe and should read 're-harmonisations'.
  6. [3.4.1] The service name 'UDIO' should be styled as 'Udio' in the participant quote and surrounding text.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper is an empirical co-design case study whose insights are inductive themes, not fitted predictions; peripheral self-citations are not load-bearing.

full rationale

This paper contains no formal derivation, fitted parameter, or mathematical prediction that could reduce to its inputs; it is an interpretive qualitative co-design case study. The design insights in Section 4 emerge from thematic analysis of workshop transcripts, participant journals, and focus-group discussions, and they are not defined in terms of one another. The prototype was developed from Workshops 1 and 2 and then used in a two-week ecological evaluation in which three of the six participants were new to the study (Section 3.4), so the evaluation is not a mere restatement of the earlier workshop data. The self-citations involving author Llano Rodriguez (e.g., references [39] and [51]) are peripheral and non-load-bearing: [39] is cited in Section 4.1 only as an example of prior Human-AI collaboration research, and [51] appears alongside other references for co-creative design; neither supports the paper's central claims by itself. The 'variation' terminology is explicitly acknowledged as a term with historical musical significance rather than a new invention, with the paper noting that the concept 'is well-established in creative research and is often described as remixing' (Section 3.2.1), so no hidden renaming is involved. A data-reporting concern does exist: Section 3.4.1 attributes the quote 'it sort of gave me something new I never really thought of before' to P7, but P7 appears in Workshop 2 (Table 2), not in the ecological evaluation participant table (Table 4), and the surrounding passage is in the ecological study section. This is a potential misattribution or data-mixing issue that affects evidence quality, but it is not circularity because the claim does not become equivalent to its inputs by construction. Similarly, the Section 4.1 insight that framing as collaborator versus tool may affect adoption is an inference from participants' stated resistance to the term 'collaborator' and the observed use of the tool-framed system, and the paper itself hedges that 'these results can not be generalised to all practising musicians.' That is an evidentiary overreach relative to the absence of a manipulated framing condition, but it is not a self-referential or fitted-parameter circularity. Overall, the circularity burden is essentially nil, with only minor peripheral self-citations and qualitative-evidence caveats noted.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim is empirical and qualitative, so there are no fitted parameters or new postulated entities. The main hidden costs are domain assumptions about the validity of self-report data, the representativeness of the prototype, the adequacy of the thematic analysis, and the ecological validity of the deployment.

assumptions (4)
  • domain assumption Participants' self-reports in workshops, journals, and focus groups are reliable evidence of their design needs.
    The study treats verbal and written reflections as the primary evidence for needs and insights, with no behavioral validation or direct observation of adoption patterns. This is invoked throughout Sections 3.1 to 3.4.
  • domain assumption The prototype built on MusicBERT and MidiFormers is a representative enough variation system to elicit generalizable reactions.
    Section 3.3 presents the tool as a basis for design insights, but the specific model, encoding, and interface could shape participant feedback in ways that do not generalize to all co-creative music AI.
  • domain assumption Thematic analysis by two researchers, followed by discussion, yields valid and unbiased themes.
    Section 3.1 describes the inductive thematic analysis method but does not report inter-rater reliability, an audit trail, or member checking, so the objectivity of the final themes is assumed.
  • domain assumption A two-week ecological deployment with AWS hosting is representative of real-world use in musicians' own environments.
    Section 3.4 uses the ecological evaluation as evidence of practical usefulness, but participants were given prompts, a 15-bar limit, and suggestions from the lead developer, which may have influenced their experience.

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Cite this review

Pith. "Pith review of Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design." pith.science (2026). https://pith.science/paper/RYKCIXF3

@misc{pith2026250209055,
  author       = {Pith},
  title        = {Pith review of: Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RYKCIXF3}},
  note         = {Machine review of arXiv:2502.09055}
}
read the original abstract

Recent advances in generative AI music have resulted in new technologies that are being framed as co-creative tools for musicians with early work demonstrating their potential to add to music practice. While the field has seen many valuable contributions, work that involves practising musicians in the design and development of these tools is limited, with the majority of work including them only once a tool has been developed. In this paper, we present a case study that explores the needs of practising musicians through the co-design of a musical variation system, highlighting the importance of involving a diverse range of musicians throughout the design process and uncovering various design insights. This was achieved through two workshops and a two week ecological evaluation, where musicians from different musical backgrounds offered valuable insights not only on a musical system's design but also on how a musical AI could be integrated into their musical practices.

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.