Culturally-responsive AI assessment systems for Ohangla music: A literature report

2026-08-21T22:15:50+00:00

The purpose of this article is to address the systematic bias in artificial intelligence (AI) music assessment systems that marginalise Indigenous African musical forms, specifically Ohangla music from Kenya’s Luo community. Current AI systems, designed around Western-centric musical paradigms, fail to recognise Ohangla’s complex polyrhythms and multifaceted cultural functions, resulting in misclassification and epistemological erasure of Indigenous musical knowledge. This study employs a systematic literature review synthesising thirty sources across ethnomusicology, AI bias research, technical music AI development, and Indigenous data sovereignty frameworks. It analyses Ohangla’s cultural significance, documents bias mechanisms in existing AI systems, evaluates technical approaches to music assessment, and examines ethical protocols for Indigenous music AI development. Findings reveal three primary mechanisms of exclusion: (1) training data bias that omits Indigenous music, (2) algorithmic assumptions privileging Western musical structures, and (3) evaluation criteria incompatible with community-centred assessment. Technical approaches succeed only in culturally homogeneous contexts, with African polyrhythms misclassified as computational “outliers” despite their mathematical sophistication. Ohangla’s role as a social technology promoting community bonding and cultural transmission challenges AI paradigms that prioritise technical optimisation over cultural preservation. The study concludes by proposing a culturally-responsive AI framework for Ohangla assessment that integrates Indigenous data sovereignty principles (OCAP®) with technical innovation. This framework provides methodological guidelines for community-centred AI development, advancing decolonising research methodologies and practical cultural preservation technology. It offers actionable recommendations for developers and policymakers to design AI systems that preserve rather than erase Indigenous cultural heritage, establishing a model for culturally-responsive AI in other Indigenous music contexts globally.

Teacher improvisation and systemic challenges in indigenous music education within the CBE framework in Nyandarua County, Kenya

2026-08-21T22:17:35+00:00

This study explored how teachers improvise in indigenous music education within the CBE context in Nyandarua County, Kenya, and the constraints they encounter. Although Competency-Based Education (CBE) has incorporated indigenous music to instil cultural identity, creativity and competency, integrating this instruction in resource-limited rural schools continues to face challenges due to a gap between curriculum policy and its implementation. The study employed a qualitative phenomenological design, purposively selected 30 primary school teachers from 10 sub-counties (Kinangop North, Kinangop South, Kipipiri, Ol Kalou, Ol Jorok, Gathanji, Njabini, Engineer, Mirangine, and Ndaragwa), and was grounded in the Culturally Responsive Pedagogy framework. Data were gathered through semi-structured interviews, classroom observations, and document analysis, and were analysed thematically. The results show that teachers relied on improvisation to maintain continuity in teaching. The practices developed to sustain curriculum continuity and engage learners were driven by severe institutional constraints during the curriculum transition. The research findings indicate that teacher-improvised instruction is important in the face of adverse circumstances, but cannot replace systematic investment. Sustainable implementation of indigenous music education within the Competency-Based Education (CBE) framework requires sustained, system-wide, and equitable pre-service teacher preparation, teacher professional development, and resource allocation to build institutional capacity and ensure effective implementation of the CBE framework with an indigenous music learning approach. This study adds to the literature by illustrating how teacher improvisation enables indigenous music instruction in a context of limited resources, whilst concealing systemic shortcomings in CBE delivery.

Journal of Music and Creative Arts

Culturally-responsive AI assessment systems for Ohangla music: A literature report

Teacher improvisation and systemic challenges in indigenous music education within the CBE framework in Nyandarua County, Kenya