#12 Ai As The New Librarian: Investigating Music Students’ And Educators’ Experiences With University Library Systems In Selected Nigerian University
UDC:
Received: Dec 12, 2025
Reviewed: Feb 01, 2026
Accepted: Feb 14, 2026
#12 Ai As The New Librarian: Investigating Music Students’ And Educators’ Experiences With University Library Systems In Selected Nigerian University
Citation: Akande, Sunday Olufemi. 2026. "Ai As The New Librarian: Investigating Music Students’ And Educators’ Experiences With University Library Systems In Selected Nigerian University." Accelerando: Belgrade Journal of Music and Dance 11:12
Abstract
The integration of Artificial Intelligence (AI) into university library systems is transforming how academic resources are accessed, organized, and utilized. This study examines the experiences and perceptions of selected music students, music educators and library staff regarding AI-enhanced library services in selected Nigerian universities. As music education demands access to diverse and often complex multimedia resources, the shift toward intelligent systems such as AI-driven search engines, automated cataloging, and virtual research assistants presents both opportunities and challenges. Using a survey research design, the study gathered data through administered questionnaires with lecturers and students in music departments in selected universities. The findings revealed a growing acceptance of AI as a valuable tool for improving research efficiency, expanding access to digital content, and supporting personalized learning. However, participants also voiced concerns about digital competence, potential overdependence on automated systems, and the diminishing interpersonal role of traditional librarians. The study concluded by recommending a balanced integration of AI and human support to ensure that library services remain accessible, relevant, and responsive to the unique needs of music education.
artificial intelligence, music education, university library systems, nigerian university, digital technologies , indigenous knowledge systems, cultural representation
Introduction
The growing relevance of digital technologies in education is reflected in how ICT tools are increasingly used to support teaching and learning, including in music education within various institutions of learning. This broader trend aligns with the ongoing integration of Artificial Intelligence (AI) into university library systems, where technologies such as machine learning, natural language processing, and intelligent search mechanisms are transforming how academic information is accessed, organized, and utilized in higher education (Cox et al. 2021). Such developments echo the findings of Akande (2019), who investigated perceptions of ICT use in the teaching and learning of music. These innovations are particularly significant in academic disciplines like music, where access to diverse multimedia content including scores, audio recordings, and historical archives is essential for effective teaching and research.
In recent years, academic libraries globally have begun adopting AI-driven systems to enhance service delivery. These include virtual reference assistants, automated cataloging, predictive analytics for resource recommendations, and semantic search engines (Wang et al. 2020; Fernández-Rodríguez et al. 2023). Such tools aim to meet the growing demand for personalized, user-centered information services in digital learning environments. However, the implementation of these technologies is not without challenges. Scholars have highlighted concerns regarding data privacy, algorithmic bias, loss of the human touch in service delivery, and the risk of widening digital literacy gaps among users (Bourg 2019; van Viersen & Lovett 2022).
In the Nigerian context, while universities have made strides toward digital transformation, the integration of AI in library systems remains uneven and underexplored, especially in niche academic areas like music education. Music departments often rely on unique information needs, including access to audio-visual materials, composition software, and culturally specific archives, which require adaptable and intelligent systems for resource retrieval (Olayemi 2020).
Understanding how AI-powered library systems are perceived and experienced by music educators and students is therefore critical in ensuring that these technologies serve the pedagogical and scholarly needs of this specialized field. This study investigates the experiences and perceptions of music educators and students in selected Nigerian universities regarding AI-driven library systems. The study aims to contribute to the growing discourse on AI in higher education by highlighting both the opportunities and limitations of these technologies in supporting academic inquiry, particularly within creative and practice-based disciplines. In doing so, it offers practical insights into how AI can be thoughtfully integrated with traditional library services to create a more inclusive and effective academic environment.
Artificial Intelligence in Academic Libraries
Artificial Intelligence (AI) is rapidly transforming the landscape of academic libraries by revolutionizing how information is organized, retrieved, and delivered. As the demand for instant access to vast volumes of digital content grows, university libraries are leveraging AI technologies to meet evolving user expectations and streamline library operations. AI applications in academic libraries now include intelligent search engines, automated cataloging systems, predictive resource recommendation tools, natural language processing (NLP) for user queries, chatbots for virtual assistance, and robotic process automation (RPA) for routine tasks (Wang, Wang & Zhang 2020).
These innovations are reshaping user experiences by increasing the speed, accuracy, and relevance of search results. For instance, AI-powered discovery systems can analyze user behavior to personalize recommendations, similar to commercial platforms like Spotify or Netflix. In their study, Cox, Pinfield, and Rutter (2021) describe this shift as part of the development of “intelligent libraries,” where machine learning algorithms not only automate backend functions but also enhance user engagement through adaptive interfaces and personalized content delivery.
This development marks a fundamental rethinking of library services from static, collection-centered spaces to dynamic, user-driven ecosystems. Furthermore, AI tools significantly enhance the management of non-textual and multimedia content, which is especially valuable in disciplines like music, art, and media studies. Through image and audio recognition, intelligent indexing, and metadata enrichment, AI helps organize diverse content types that traditional library systems often struggle to manage. For example, Zhang, Liu, and Chen (2021) demonstrate how deep learning models can be used in music libraries to recommend compositions based on tonal similarities, user preferences, or even mood tags derived from song attributes. These capabilities are increasingly important as academic libraries move beyond print and text to incorporate rich media formats.
However, the adoption of AI in library systems is not without controversy. Several scholars have raised concerns about the ethical implications of AI-driven automation in libraries. Bourg (op. cit.) warns that while AI can significantly reduce workload and improve operational efficiency, it may also lead to the marginalization of human librarians and erode the relational, mentoring roles that are central to academic support. The over-reliance on AI could potentially result in a loss of critical human judgment, particularly in tasks that require cultural sensitivity, ethical reasoning, or contextual understanding.
In addition, researchers such as Fernández-Rodríguez, López-Borrull, and Cobarsí-Morales (2023) caution against algorithmic bias and the opaque nature of AI decision-making processes. These scholars argue that because AI systems are often trained on data sets that reflect existing inequalities or cultural assumptions, they may inadvertently reinforce bias, marginalize minority voices, or recommend materials that align with dominant narratives. This raises important concerns for equity, diversity, and academic freedom in university libraries.
Privacy and data protection also remain major concerns. AI systems often rely on the collection and analysis of user data to function effectively. As highlighted by van Viersen and Lovett (op. cit.), without clear policies on data governance and ethical use, AI-powered library services risk violating user privacy and autonomy. These concerns are particularly pressing in regions like Nigeria, where data protection laws and institutional policies may be underdeveloped or poorly enforced.
Moreover, researchers have noted that successful implementation of AI in academic libraries depends on institutional readiness, including infrastructure, staff training, and user digital literacy. According to Ifijeh and Yusuf (2020), many university libraries in developing countries face significant challenges in deploying AI systems due to inadequate funding, inconsistent internet access, and a lack of technical expertise. They argue that for AI integration to be meaningful and inclusive, it must be accompanied by strategic investment in digital capacity building for both library staff and users.
It then means that, while AI presents transformative opportunities for academic libraries in enhancing service delivery and user experience, its adoption must be approached with critical awareness of its limitations and ethical implications. A balanced model that integrates AI tools with human expertise particularly in user education, content curation, and ethical oversight offers a more sustainable and inclusive pathway for the future of academic librarianship.
Music Education and Library Resource Needs
Music education, by its very nature, is a multifaceted discipline that requires access to a broad spectrum of complex and multimedia-rich resources. Unlike many traditional academic fields that rely predominantly on text-based materials, music education engages with an array of content types, including sheet music, audio and video recordings, music theory textbooks, composition and notation software, historical archives, and live performance analyses (Olayemi op. cit.). These diverse resources are essential not only for theoretical instruction but also for practical training in performance, composition, and music appreciation.
The unique structure of music-related content poses significant challenges for traditional library systems, which are often designed around static, text-based cataloging and retrieval models. As such, organizing, indexing, and facilitating access to multimedia music resources requires advanced systems capable of managing audio-visual formats, time-based metadata, and dynamic user interfaces. Olayemi notes that most Nigerian university libraries still operate with outdated cataloging systems that are ill-equipped to handle the complexities of music collections, particularly those involving indigenous and oral traditions that are not easily codified in conventional metadata schemas (Ibid.)
In response to these limitations, Artificial Intelligence (AI) technologies offer transformative possibilities for music education and the libraries supporting it. AI-powered systems can enhance resource accessibility through features such as automated tagging of audio and video files, real-time transcription of performances, and semantic search capabilities. Machine learning algorithms, for instance, can be trained to recognize melodic patterns, harmonic structures, or rhythmical features in digital music files, thereby facilitating more accurate and meaningful search results for music students and researchers (Zhang, Liu & Chen op. cit.). Similarly, AI-enabled recommendation engines can suggest relevant compositions, scholarly articles, or performance recordings based on user preferences and previous interactions, mirroring the personalized learning environment now expected in digital education platforms.
Beyond search and recommendation functionalities, AI tools can also support the pedagogical dimensions of music education. For example, intelligent tutoring systems (ITS) and AI-assisted music analysis platforms can provide real-time feedback on student performances, assist in music composition through generative algorithms, and simulate ensemble experiences in virtual learning environments (Shrestha & Yang 2020). Such innovations expand the boundaries of what is possible in music education, particularly in remote or resource-constrained settings.
However, the implementation of AI-driven solutions in music education is not without its challenges, especially within the Nigerian higher education context. Seldon and Abidoye (2022) argue that while AI presents numerous advantages, its effective adoption is contingent upon several critical factors, including digital infrastructure, institutional investment, and user competence. Many Nigerian universities lack the necessary bandwidth, technical support, and digital repositories to fully leverage AI capabilities in their library systems. Moreover, the specialized nature of music content requires domain-specific AI models and metadata frameworks, which are often absent in generic library systems.
User readiness is another crucial concern. For AI-powered music library systems to be beneficial, both students and educators must possess a certain level of digital literacy and familiarity with the technologies involved. Unfortunately, digital literacy remains unevenly distributed across Nigerian institutions. According to Ifijeh and Yusuf (op. cit.), many students, particularly those in non-STEM fields struggle to adapt to rapidly evolving digital tools due to inadequate exposure and insufficient training opportunities. This gap can significantly hinder the ability of users to engage meaningfully with AI-enhanced systems, thereby limiting their potential impact.
Furthermore, there are epistemological considerations regarding how AI systems interpret and classify music. Traditional AI models struggle with non-Western musical forms, oral traditions, and improvisational performance styles that are prevalent in African music education. Scholars such as Nketia (2019) have emphasized the importance of preserving cultural context and indigenous knowledge systems within music archives. Thus, reliance on AI tools trained primarily on Western datasets may inadvertently marginalize local content, leading to a homogenization of musical knowledge in academic libraries. The successful integration into Nigerian university libraries requires careful consideration of technological readiness, user capability, and cultural relevance. Libraries must adopt inclusive strategies that combine AI efficiency with local knowledge frameworks and human-centered design in order to truly enhance music education in a sustainable and equitable way.
Adoption of AI in Libraries
The successful integration of Artificial Intelligence (AI) in academic libraries is not solely a technological issue; it fundamentally hinges on acceptance, and digital literacy. AI systems are most effective when users not only understand how to operate them but also trust their outputs and feel confident navigating digital environments. According to Fernández-Rodríguez, López-Borrull, and Cobarsí-Morales (op. cit.), students often demonstrate a positive attitude towards AI-enabled library services, appreciating their speed, convenience, and personalized features such as automated recommendations and instant access to resources. These tools are particularly attractive to digital-native students who have grown up interacting with AI-driven platforms in their everyday lives.
However, staff, music students and music educators tend to adopt a more cautious stance. While acknowledging the efficiency of AI tools, many express concern about the over-reliance on algorithmic systems, especially in areas that require critical engagement and nuanced interpretation of information. As Fernández-Rodríguez et al. point out, educators worry that AI may contribute to a decline in critical thinking by presenting users with pre-filtered information that discourages independent inquiry or deeper intellectual exploration (Ibid.). This tension reflects broader anxieties about the automation of cognitive tasks traditionally mediated by human expertise.
In the Nigerian context, user readiness emerges as a particularly critical issue. Omoniyi (2021) observes that the adoption of AI in Nigerian university libraries is hampered by limited exposure to emerging technologies, poor ICT infrastructure, irregular power supply, and a lack of training for both staff and students. Music educators, for example, may find AI-powered systems helpful for discovering digital scores or recordings, but struggle with user interfaces that are not optimized for music-specific formats or metadata. Furthermore, the technical language used in many AI systems can alienate users with limited digital literacy, creating a barrier to effective adoption and engagement.
To note is the emotional and ethical dimension of AI integration. The prospect of AI systems replacing human librarians has sparked significant concern among library professionals and users alike. Van Viersen and Lovett (op. cit.) argue that while AI can enhance efficiency, it cannot replicate the human elements of academic support, particularly in disciplines that rely on mentorship, interpretive dialogue, and emotional engagement, such as music and the arts. Human librarians play critical roles not just in information retrieval but also in helping students navigate complex research questions, providing contextual insight, and offering encouragement throughout the academic journey. In creative fields, these relational dynamics are central to the learning process and cannot be easily substituted by machines.
In line with this view, Bourg (op. cit.) emphasizes the importance of rehumanizing the library in the face of growing automation. She advocates for a “human-in-the-loop” approach, where AI serves to support, rather than replace, human librarians. This model ensures that while users benefit from the speed and scalability of AI tools, they also retain access to the interpretive and empathetic support of trained professionals. Such a hybrid model is particularly valuable in music education, where the emotional, cultural, and performative aspects of learning demand more than just data-driven responses.
Invariably, to design AI systems that are contextually appropriate and user-friendly, and to ensure that users are adequately trained to interact with these systems. For AI integration to be meaningful in Nigerian academic libraries, especially within music departments, there must be deliberate investment in digital literacy programs, infrastructure development, and participatory design processes that involve both students and educators. As Adeyemo and Onifade (2022) suggest, sustainable adoption of AI in Nigerian universities must be underpinned by inclusive policy frameworks that prioritize equity, capacity-building, and cultural relevance.
In light of this it shows that readiness are not peripheral concerns, they are central to the effectiveness of AI in academic library contexts. Without trust, accessibility, and competence, even the most sophisticated AI systems risk underutilization or outright rejection by the very communities they are intended to serve. This underscores the need for a balanced, human-centered approach to AI adoption; one that places user empowerment at the core of technological innovation.
The Future of AI and Human Collaboration in Library Services
Contemporary discourse on the future of academic libraries increasingly emphasizes the necessity of adopting a hybrid model that marries the computational efficiency of Artificial Intelligence (AI) with the nuanced expertise of human librarians. Rather than viewing AI as a replacement for librarians, scholars such as Bourg (op.cit.) advocate for AI to serve as an augmentative tool; one that enhances librarians’ capacity to perform complex, higher-order tasks that require critical thinking, professional judgment, and interpersonal engagement. These tasks include research consultation, expert curation of resources, instructional support, and the facilitation of scholarly inquiry. In this hybrid framework, AI undertakes routine, time-consuming processes such as cataloging, metadata enrichment, and preliminary search filtering, thereby freeing librarians to devote more time and intellectual energy to mentoring, personalized guidance, and the interpretation of academic content.
This approach is especially salient in disciplines like music education, where the nature of knowledge is deeply interpretive, context-sensitive, and embedded within cultural and historical frameworks. Music librarianship extends beyond mere resource provision to encompass interpretive analysis, the preservation of heritage, and the facilitation of aesthetic judgment; all of which are inherently human activities resistant to full automation. For example, while AI can efficiently index audio files or recommend scores based on algorithmic similarity, it cannot replicate the librarian’s role in advising students on the historical significance of a composition, the stylistic nuances of a performance tradition, or the ethical considerations involved in sourcing indigenous musical materials. These dimensions require empathy, cultural literacy, and critical insight qualities that remain uniquely human.
The pedagogical implications of this hybrid model suggest that academic library services should be designed with a balanced integration of AI and human resources, aligned with educational goals and cultural sensitivities. Such an integrative approach ensures that AI acts as an enabler of knowledge discovery, while librarians retain their central role as educators, mentors, and cultural custodians. This perspective aligns with emerging frameworks in information science that promote “human-in-the-loop” systems, where AI tools assist but do not replace human decision-making (Bourg op. cit; Van Viersen & Lovett op. cit.).
Moreover, the integration of AI must be culturally appropriate and inclusive, particularly in contexts like Nigerian higher education where the diversity of musical traditions and educational needs is profound. Policies guiding AI adoption should prioritize equitable access, user empowerment, and respect for indigenous knowledge systems, thereby avoiding the risks of technological determinism or cultural homogenization. A balanced hybrid model not only enhances operational efficiency but also preserves the humanistic values central to academic librarianship.
Music Educators
| S/N | Items | SA | A | SD | D |
|---|---|---|---|---|---|
| 1 | AI-powered systems in my university library have improved the accessibility of music-related resources. | 25% | 40% | 20% | 15% |
| 2 | I feel confident using AI-based tools to support my teaching and research in music education. | 20% | 36% | 24% | 20% |
| 3 | The current AI features in the library are relevant and adaptable to the needs of music educators | 22% | 38% | 18% | 22% |
| 4 | AI systems help reduce the time spent searching for appropriate music scores, recordings, and scholarly materials | 30% | 40% | 16% | 14% |
| 5 | AI tools do not adequately represent or preserve indigenous Nigerian music traditions | 18% | 32% | 30% | 20% |
| 6 | There is adequate training provided for lecturers on how to use AI tools in the university library system. | 14% | 26% | 35% | 25% |
| 7 | AI systems currently in use often fail to accommodate the multimedia nature of music education. | 20% | 35% | 26% | 19% |
| 8 | Human librarians are still essential for contextualizing and curating music resources despite the rise of AI. | 34% | 41% | 15% | 10% |
| 9 | I would prefer a hybrid library system that combines AI tools with expert human support. | 39% | 41% | 10% | 10% |
| 10 | AI tools have the potential to transform music education positively if aligned with cultural and academic needs | 30% | 38% | 18% | 14% |
Music Students
| S/N | Items | SA | A | SD | D |
|---|---|---|---|---|---|
| 1 | It is easier to access music scores, recordings, and textbooks through AI-powered library systems | 28% | 42% | 18% | 12% |
| 2 | AI features in my university library have helped me discover useful music-related materials more quickly | 25% | 40% | 22% | 13% |
| 3 | There is more confident using AI-driven tools such as intelligent search engines or recommendation systems in the library | 30% | 38% | 20% | 12% |
| 4 | My university provides adequate training or support on how to use AI-based features in the library system | 18% | 32% | 28% | 22% |
| 5 | The AI systems in the library are effective in managing and organizing audio and video materials relevant to my music studies | 26% | 39% | 21% | 14% |
| 6 | AI systems in the library does not properly represent Nigerian or African music traditions. | 22% | 34% | 26% | 18% |
| 7 | Using AI in the library has improved the way I study or do research in music. | 29% | 41% | 19% | 11% |
| 8 | It is preferable to speak with a librarian over using AI tools when searching for music materials. | 33% | 37% | 18% | 12% |
| 9 | The AI systems in the library are not always user-friendly for music students like me | 20% | 35% | 25% | 20% |
| 10 | Combination of AI tools and human support would give me the best learning and research experience in the music library. | 40% | 42% | 10% | 8% |
Discussion of Findings
The study reveals a broadly positive perception of AI's role in music education libraries, particularly in enhancing accessibility and research efficiency. A significant 65% of educators and 70% of students agreed that AI-powered systems improved access to music-related resources such as scores, recordings, and scholarly materials. Similarly, 70% of educators and 65% of students affirmed that AI tools reduced the time spent searching for relevant materials. These findings align with previous studies that highlight AI's capacity to streamline information retrieval and improve access to digital content in educational settings (Chatterjee & Bhattacharjee 2020; Luckin et al. 2016).
Despite these benefits, confidence in using AI tools varied. While 68% of students expressed confidence in using AI-driven systems, only 56% of educators felt similarly. This disparity may reflect generational differences in digital fluency and comfort with emerging technologies, a trend noted by Selwyn (2019), who observed that faculty adoption of AI tools is often constrained by a lack of training and institutional support. Concerns about the relevance and adaptability of AI systems also emerged. While 60% of educators and 65% of students believed the current AI features were relevant and effective, particularly in managing multimedia resources. Respondents questioned whether these tools adequately support the performance-based and multimodal nature of music education. Holmes et al. (2022) similarly cautioned that AI systems often lack the flexibility required for creative and discipline-specific tasks.
A major issue raised was the lack of adequate training. Only 40% of educators and 50% of students agreed that their universities provided sufficient support to effectively utilize AI tools. The remaining respondents expressed dissatisfaction, suggesting a significant gap in AI literacy and institutional preparedness, further reinforcing the findings of Ahmed and Al Dhubaib (2019), who highlighted the importance of infrastructure and training in the successful implementation of AI in education. Cultural representation was another area of concern. Around 50% of educators and 56% of students felt that AI systems fail to properly represent indigenous or African music traditions.
While not universally acknowledged, this concern underscores the need for culturally responsive AI systems that reflect and preserve diverse musical heritages (Omoera 2021). Importantly, both groups emphasized the enduring role of human librarians. A strong majority 75% of educators and 70% of students valued human support, with 80% of educators and 82% of students favoring a hybrid system that combines the speed of AI with the contextual expertise and empathetic guidance of librarians. This preference reinforces the argument that AI should serve as a complement, not a replacement, to human knowledge workers (Frank et al. 2019).
Finally, optimism about AI’s transformative potential was high, with 68% of educators and 70% of students agreeing that AI can significantly enhance music education—provided it is tailored to academic, cultural, and pedagogical needs. This conditional optimism suggests that while users are open to innovation, successful adoption depends on context-aware, discipline-specific implementation.
Interview
In this study, (Layemi 2025) reported that student traffic to the library has remained steady, with over 60% of students using its facilities daily. Initial challenges with network connectivity were said to have significantly improved. The integration of Artificial Intelligence (AI) into academic library services was described as having a positive impact, particularly in enhancing access to digital resources and improving research productivity. Although visits for physical materials have declined largely due to the increased use of e-resources and AI tools; the library continues to serve as a space for digital access and academic collaboration rather than traditional book lending.
It means, the user behavior illustrates a broader trend in academic environments where libraries are transitioning from physical repositories to digital and collaborative learning hubs. AI appears to be facilitating this transformation by making access to scholarly content more efficient and responsive to users' evolving needs. Odunewu (2025) opines that the adoption of AI in the library has reduced the need for physical visits, as users increasingly access resources online. AI tools were identified as being commonly used for assignments, content generation, proposal writing, and grant applications.
However, concerns were raised about the reliability of such content, given that plagiarism detection systems can identify paraphrased or structurally manipulated text. Despite recognizing AI's usefulness, a strong emphasis was placed on maintaining scholarly integrity through peer review and compliance with academic standards. It was also stressed that traditional library services should remain intact, with AI functioning as a supportive rather than substitutive tool. Consequently, it becomes imperative to note the ethical implications of AI in academic contexts. While automation and content generation tools can enhance productivity, they also present challenges around originality and academic honesty. Balancing innovation with ethical standards is essential in sustaining the credibility of scholarly output.
Onifade (2025) referred to AI as the “new librarian,” drawing attention to its expanding role in digital humanities and research. AI was described as a tool that accelerates access to information, aids in structural content development, and supports the discovery of both contemporary and historical sources. The need for AI literacy among students and staff was emphasized, with institutional efforts underway to subscribe to various AI tools to support digital library services. Nonetheless, a clear distinction was made between AI's functional capabilities and the irreplaceable role of human librarians, particularly in offering contextual understanding, critical judgment, and empathetic user engagement. As stated
The human element of librarianship remains irreplaceable. AI is a tool, not a substitute. (Ibid.)
Onifades’ position reflects a nuanced understanding of AI's place in academic libraries not as a replacement for librarianship but as a catalyst for enhancing its digital dimensions (Ibid.). The emphasis on AI literacy underscores the importance of equipping users with the skills to use these tools effectively and responsibly.
Conclusion
The study reveals a broadly positive perception of AI's role in music education libraries, particularly in improving access to music-related resources and enhancing research efficiency. Both educators and students acknowledged that AI tools help reduce the time spent searching for materials, aligning with prior research on AI’s ability to streamline information retrieval in educational settings.
However, confidence in using AI tools varied, with students generally more comfortable than educators, likely due to differences in digital fluency and exposure. Concerns were raised about the relevance and adaptability of AI systems, particularly whether they sufficiently support the multimedia and performance-based nature of music education. This echoes broader cautions about AI’s flexibility in handling creative and discipline-specific tasks.
A significant issue identified was the lack of adequate training and institutional support, highlighting a gap in AI literacy that limits effective adoption. Both educators and students also expressed concern about the limited cultural representation in AI systems, particularly regarding indigenous and African music traditions, emphasizing the need for culturally inclusive AI.
The study underscores the continued importance of human librarians, with a strong preference for hybrid models combining AI’s efficiency with the contextual knowledge and empathetic support provided by humans. Interviews further emphasized AI’s positive impact on access and research productivity, while also highlighting challenges related to academic integrity and ethical use. AI was viewed as a powerful tool that enhances, but does not replace, the critical role of human librarians. Overall, there is cautious optimism about AI’s transformative potential in music education, contingent on thoughtful, context-aware implementation that addresses cultural, pedagogical, and ethical considerations. Emphasizing AI literacy and balanced integration remains key to harnessing its benefits effectively.
References
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Interviewees
- Layemi, T. 2025. Olabisi Onabanjo University, Ago-Iwoye, 3rd October, 2025
- Odunewu, M. 2025. Olabisi Onabanjo University, Ago-Iwoye, 3rd October, 2025
- Onifade, A. 2025. Olabisi Onabanjo University, Ago-Iwoye, 3rd October, 2025
