The paper addresses what Myers identifies as a blind spot in contemporary feedback research. Over the past two decades, scholars have built sophisticated accounts of feedback as dialogue, emphasising student agency, feedback literacy and the active uptake of comments. Students are increasingly encouraged to seek, interpret and use feedback rather than passively receive it. Yet, Myers contends, this literature has had much less to say about what feedback does to students in relation to knowledge and knowing. Feedback does not merely convey information about how well a task was performed; it communicates judgements about whether the student’s thinking is worth taking seriously, and those judgements can have consequences that outlast any single assignment.

To build this argument, the study brings three theoretical traditions into conversation. The first is recent scholarship on epistemic agency in assessment, which examines how assessment practices shape students’ capacity to act as knowers rather than merely as recipients of knowledge. The second is Gert Biesta’s influential account of the three purposes of education: qualification, socialisation and subjectification. Qualification concerns the knowledge and skills students acquire; socialisation concerns their induction into traditions, norms and practices; and subjectification concerns the emergence of the student as an independent thinking subject, someone capable of existing in the world in their own distinctive way. The third tradition is Miranda Fricker’s theory of epistemic injustice, which describes wrongs done to people specifically in their capacity as knowers.

Fricker’s framework distinguishes two main forms of epistemic injustice. Testimonial injustice occurs when a speaker’s testimony is given deflated credibility because of prejudice related to their identity, so that the speaker is wronged as a giver of knowledge. Hermeneutical injustice occurs when gaps in shared interpretive resources leave people unable to make sense of their own experiences. Myers focuses primarily on the testimonial form, arguing that written feedback is a paradigmatic site for it: the marker is, in effect, a credibility assessor whose judgements about a student’s claims are shaped by perceptions of the student as well as by the content of the work itself. When those credibility judgements are driven by structural prejudice rather than by genuine engagement with the student’s thinking, the student suffers a distinctive kind of harm.

Read together, these perspectives reveal what Myers describes as an unresolved tension at the heart of feedback theory. Feedback is supposed to serve socialisation: it inducts students into disciplinary norms, showing them how arguments are constructed, what counts as evidence, and how a field writes and thinks. But the very same judgements that accomplish this induction can simultaneously foreclose the student’s emergence as an independent epistemic subject. A student whose contributions are systematically discounted, whose voice is heard as deficient rather than different, learns not only the discipline’s conventions but also a corrosive lesson about their own entitlement to know. In Biesta’s terms, socialisation can colonise subjectification, crowding out the educational purpose that many educators regard as the most important one.

The paper grounds this abstraction by drawing on documented feedback policy to specify the institutional conditions of the encounter. Feedback is not delivered in a vacuum; it is shaped by institutional frameworks, quality assurance agendas, turnaround times and rubrics that predefine what legitimate academic performance looks like. Within these structures, the marker acts as an institutionally authorised credibility judge. The authority of the judgement derives not only from the marker’s expertise but from the institution’s backing, which means that a deflating credibility assessment carries official weight. When structural prejudice, whether related to language, race, gender or cultural background, shapes that assessment, the injustice is not an individual failing of a careless grader but a systematic feature of the encounter itself.

The argument has sharp relevance for research on bias in academic judgement. Empirical studies have documented, for example, that student evaluations of teaching exhibit gender biases, sometimes detectable even in controlled online settings where the same instructor is presented under different identities. Scholarship on race and assessment in higher education has similarly conceptualised barriers facing minoritised students and argued for measurable change. Myers’s framework offers a theoretical account of why such biases matter beyond their effect on marks: they operate as testimonial injustices within credibility encounters, distorting the student’s standing as a knower and thereby undermining subjectification in Biesta’s sense. The injustice is not simply that the grade may be wrong, but that the student is wrongly positioned in relation to knowledge itself.

One of the most timely dimensions of the paper concerns automation. Myers specifies how the mechanism he describes operates when credibility judgements are delegated to automated instruments, a situation that has become increasingly common with the spread of artificial intelligence tools in assessment. When a judgment about credibility is produced by a machine, the resulting credibility deficit acquires the appearance of measurement, of objective fact rather than contestable interpretation. This matters because automated systems can inherit and amplify structural prejudice. Research has shown, for instance, that GPT-based detectors are systematically biased against non-native English writers, flagging their authentic writing as machine-generated more often than that of native speakers. Scholars of AI ethics have explicitly analysed epistemic injustice in generative AI, and assessment researchers have begun developing recognition-based frameworks for understanding the place and value of AI-generated feedback.

In the automated case, the epistemic wrong is layered. The student is subject to a deflating credibility judgement, but the source of that judgement is an instrument whose authority is anchored in the aura of quantitative objectivity. Contesting the judgement means contending not with an individual marker but with an opaque system, which raises the barriers to challenge precisely as the stakes rise. Myers’s point is that this is not merely a technical problem to be solved with better algorithms. Epistemic injustice in feedback, he argues, is not simply a failure of technique but a distortion of educational purpose. A system can be efficient, consistent and well designed, and still function unjustly if its credibility judgements are structurally prejudiced or if they foreclose rather than foster the student’s development as an independent thinker.

The paper’s conclusion carries implications for institutions, educators and researchers alike. If feedback is a credibility encounter, then improving feedback means attending not only to the clarity, timeliness and actionability of comments, but to the conditions under which students are recognised as legitimate knowers. That requires scrutinising the structural prejudices that shape judgements, interrogating the delegation of those judgements to automated instruments, and asking hard questions about whether current feedback cultures serve socialisation at the expense of subjectification. Recent work on feedback cultures in universities, including analyses of strategy documents from more than a hundred UK institutions, suggests that accountability and quality assurance agendas already shape how feedback is enacted, often in ways that sit uneasily with dialogic ideals. Myers’s contribution is to name precisely what is at stake when those agendas collide with students’ standing as knowers: nothing less than whether higher education fulfils its promise of bringing new thinking subjects into the world, or quietly teaches many of them that their voices do not count.

Subject of Research: Epistemic injustice in written feedback and student credibility in higher education

Article Title: Feedback as a credibility encounter: epistemic injustice and the foreclosure of subjectification

Article References: Myers, T. (2026). Feedback as a credibility encounter: epistemic injustice and the foreclosure of subjectification. Higher Education. https://doi.org/10.1007/s10734-026-01797-w

Image Credits: AI Generated

DOI: 10.1007/s10734-026-01797-w

Keywords: epistemic injustice, feedback, higher education, assessment, testimonial injustice, subjectification, epistemic agency, credibility, Gert Biesta, Miranda Fricker, artificial intelligence, feedback literacy

Fricker’s framework distinguishes two forms of epistemic injustice. Hermeneutical injustice occurs when a group lacks the interpretive resources, such as language, technology or theory, needed to make its experiences understood and validated by others. Testimonial injustice occurs when the credibility of a person or community as a knower is devalued because of prejudice or unequal power dynamics. The authors apply both concepts to the African musical context, arguing that AI systems wrong African communities twice over: first by failing to interpret and represent their musical knowledge accurately, and second by dismissing the credibility of that knowledge within technological infrastructures that privilege Western forms of documentation.

The technical evidence for this bias is substantial. Analyses of prominent training databases such as the Million Song Dataset and the GTZAN Genre Collection show they are heavily skewed toward Western repertoire. One 2025 study of music generation datasets found that Western genres account for roughly 94 percent of total dataset hours, while Middle Eastern, Latin American, South Asian, Oceanian, Central Asian and African music together make up just 5.7 percent. Research on the MuseData and Lakh MIDI datasets, staples of computational musicology, found that less than 6 percent of AI music training data is non-Western. The consequences are measurable: a 2026 empirical study found that two major commercial generators, Suno AI and Udio, failed to accurately reproduce Kenyan Benga melodies when asked to produce African music, as confirmed by sonic analysis and interviews with music experts.

The problem runs deeper than simple underrepresentation. Many African musical traditions rely on complex rhythmic structures, sliding notes and layered polyrhythms that resist the fixed beat and chord embeddings on which generative models depend. A genre adaptation experiment with Hindustani Classical and Turkish Makam, two underrepresented non-Western traditions, showed that while models possessed some adaptive capacity, their performance was significantly inconsistent compared with their Western baselines. Because much African music is transmitted orally rather than through formal notation, it is also harder to encode into the representational formats that deep learning requires. Even when African music is formalised in written form, it may still be marginalised by the dominance of English and particular digital classification frameworks.

The result, the authors argue, is a flattening of extraordinary diversity into a generic, exoticised sound. AI generative platforms tend to merge many distinct genres into a generalised African style of simplified polyrhythms, indigenous instruments and microtonal scales, stripping away the uniqueness and authenticity of individual traditions. For the Ewe people of Ghana, whose tonal language shapes songs with distinctive sliding notes and beat timing, this misrepresentation constitutes hermeneutical injustice: their knowledge is not merely absent from the systems but distorted when it appears. The long-standing Western tendency to view African musical structures as simplistic or monotonous compounds the harm, dismissing traditions refined over generations.

Why does this matter beyond aesthetics? Because in many African societies, music is not reducible to entertainment. It functions as a repository of indigenous knowledge, transmitting values, morals, ritual practices, history and social cohesion across generations. Among the Yoruba, drumming traditions announce births and communal meetings. Xhosa traditional healers use music and dance to connect with ancestors and heal physical and spiritual ills. The Krobo people of Ghana use music in the Dipo ceremony to educate girls entering puberty. Shona women in Zimbabwe deploy songs to fight social ills and condemn patriarchal behaviour, while anti-apartheid songs such as Stimela and Soweto Blues helped build value systems of resistance in South Africa. When AI platforms misrepresent or omit these traditions, they threaten the knowledge encoded within them.

The study situates this pattern within a longer colonial history of cultural extraction. Mid-twentieth-century missionaries appropriated indigenous sounds for Christian worship, and the explorer David Fanshawe recorded African singers whose voices earned him royalties while the performers were excluded from payment records. Contemporary AI systems, the authors suggest, extend this lineage: African music can be extracted, commodified and circulated by Global North companies without credit or compensation to the communities whose cultural labour sustains the systems. Legal cases sharpen the concern. The singer Asha Bhosle took her dispute with AI platforms to the Bombay High Court after her voice was used to train models without permission, and South African singer Yvonne Chaka Chaka announced legal action after her voice and image appeared in an AI-generated advert without her consent.

Opacity compounds the problem. Companies rarely disclose which recordings and datasets are used to train their models. Suno’s training practices came to light only through legal proceedings, which revealed the use of millions of songs, including copyrighted material, yet no publicly accessible song-level training registries or comprehensive model cards exist. Without transparency, African artists cannot know whether their work has been used, seek attribution, or consent to or refuse its inclusion. Meanwhile, large language models used for lyric writing exhibit their own biases: research presented in 2024 found that ChatGPT displays musical ethnocentrism, consistently rating African and Asian musical cultures lower than Western ones. Since most AI music generators operate only in dominant languages such as English, vernacular expression with spiritual and emotional significance is excluded, a serious concern given that the United Nations estimates an indigenous language becomes extinct every fortnight.

The economic stakes are considerable. An Amapiano artist in South Africa using today’s AI tools would find the platforms struggling to produce the local sound, while an American pop artist receives an effective creative assistant, reinforcing Western music’s commercial dominance. The existing market disparity is stark: South Africa’s recorded music market stood at roughly US$82.5 million in 2024 against US$17.67 billion in the United States. The Southern African Music Rights Organisation has warned that blurring lines between human creation and algorithmic imitation could undermine the epistemic authority of local musicians, and the South African Cultural Observatory has reported creative workers’ fears of a new data colonialism, in which multinational companies extract value from Africa and sell back commodified versions.

The authors are careful to note that the mere existence of AI music systems does not automatically cause epistemic injustice, and that artists can theoretically opt out. But market incentives, algorithmic recommendations, streaming playlists and publishing norms create what they call a soft, indirect compulsion that shapes which music is considered credible. Their proposed remedies include mandatory transparency about training data, independent audits and standardised benchmarks for evaluating how AI represents non-Western traditions, community-consent protocols, collective licensing and benefit-sharing arrangements that recognise communal rather than individual authorship, and capacity building so African communities can train their own systems and exercise epistemic authority over how algorithms understand their culture. The study, being an integrative literature review rather than empirical fieldwork, acknowledges its conceptual limits, but its message is urgent: algorithms are not neutral, and without deliberate intervention, AI music platforms risk becoming the latest chapter in a long history of silencing African knowledge.

Subject of Research: Epistemic injustice in AI-generated music and its impact on African indigenous musical knowledge systems

Article Title: Turn down the volume! How AI music platforms carry out epistemic injustice and silence African traditions

Article References: Maisiri, J., & Ncube, A. T. (2026). Turn down the volume! How AI music platforms carry out epistemic injustice and silence African traditions. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03327-9

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03327-9

Keywords: artificial intelligence, AI music generation, epistemic injustice, African music, indigenous knowledge systems, cultural bias, algorithmic bias, digital colonialism, Miranda Fricker, training datasets, music industry, AI ethics