INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI November 2025| Special Issue on  
Integrating AI and Emerging Technologies in Academic Libraries for  
Efficient Service Delivery  
1K.T. Nwala, PhD., 2C. E. Otuza, PhD., 1O. S. Johnson  
1Department of Mathematics and Computer Science Clifford University, Owerrinta, Nigeria  
2Department of Library & Information Science Clifford University, Owerrinta, Nigeria  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0744  
Received: 03 December 2025; Accepted: 09 December 2025; Published: 20 December 2025  
ABSTRACT  
Libraries have historically played a vital role in promoting equal access to knowledge, lifelong learning, and  
community engagement. In light of rapid technological advancements, libraries are increasingly expected to  
adopt emerging tools such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), cloud  
computing, RFID systems, and the Internet of Things (IoT). These technologies offer significant potential to  
enhance library functions including search capabilities, user interaction, personalized recommendations, and  
inventory management. However, without strategic guidance, their implementation risks being inconsistent,  
ethically problematic, or unsustainable. This study identifies best practices for the responsible integration of  
these technologies in library environments. A systematic literature review was complemented by a descriptive  
analysis of survey responses from 27 academic libraries across 11 Nigerian universities. The survey assessed  
trends in adoption, staff readiness, user satisfaction, and implementation strategies. Results indicate that while  
72% of libraries use RFID systems and 58% utilize cloud platforms, only 18% have adopted VR/AR or AI-based  
services, reflecting a cautious approach toward high-cost and complex technologies. Successful integration  
requires more than technical deployment it demands user-centered design, staff training, community  
involvement, and strong data privacy policies. By adopting these strategies, libraries can responsibly leverage  
emerging technologies to improve service delivery while upholding their core values of equitable access and  
public trust. This ensures that libraries remain relevant, inclusive, and adaptable within an evolving digital  
information landscape.  
Word Count: 226  
INTRODUCTION  
Academic libraries have undergone a profound transformation in the digital age, evolving from static repositories  
of printed materials into dynamic knowledge hubs enriched by technology. The traditional emphasis on physical  
collections and in-person reference has shifted towards online catalogs, digital repositories, and integrated  
learning spaces such as Learning Commons—designed to facilitate student collaboration, research, and  
technology access in a more seamless environment (Learning Commons, n.d.). As users increasingly demand  
seamless, personalized, and on-demand services, academic libraries are under growing pressure to adopt  
innovative, user-centered models. “Library 2.0” concepts emphasize interactive, participatory platforms that  
support customization, user reviews, and social engagement—aspects that empower users to shape their own  
information experience (Library 2.0, n.d.). Moreover, academic libraries are expanding services such as  
makerspaces, digital content access, and mobile apps to meet diverse scholarly and creative needs in a highly  
connected world (Lucky University library app launch, 2025). Emerging technologies, and in particular Artificial  
Intelligence (AI), are playing an increasingly central role in transforming how academic libraries deliver  
services. AI applications now include automatic cataloging, metadata generation, and recommendation systems  
that tailor resource suggestions based on individual user behavior (Recommender system, n.d.). These systems  
enhance discovery and reduce workload for librarians, enabling more efficient and personalized service delivery.  
In developing economies, scholars have highlighted AI’s utility in technical services such as indexing,  
acquisition, shelf reading, and reference support (Oseji etꢀal., 2023). Recent systematic reviews chart a surge in  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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research on AI in library contexts between 2019 and 2023, indicating rising scholarly interest. Most AI-related  
library studies emerged from countries like England, the United States, and Germany, covering topics such as  
automation of metadata workflows and intelligent retrieval services (South African Journal of Libraries and  
Information Science, 2019–2023). Additional reviews emphasize AI’s transformational potential in academic  
libraries delivering 24/7 virtual reference, improving accuracy in cataloging, and supporting predictive analytics  
for collection development and resource allocation (Review of AI implementation, n.d.). AI-powered tools have  
also been used to automate labor-intensive tasks such as systematic literature reviews by extracting and  
synthesizing relevant data from large corpora, freeing researchers to focus on interpretation rather than manual  
screening (Torre-López etꢀal., 2024). Meanwhile, augmenting physical library spaces with AR technologies such  
as head-mounted display systems that gamify browsing and enhance discovery demonstrates how emerging tech  
can revitalize in-person visitor engagement (Wei etꢀal., 2024).  
Despite the promise of AI and emerging technologies, integration into academic libraries faces barriers,  
especially in resource-limited settings. Common challenges include unstable power supply, limited ICT  
infrastructure, lack of trained personnel, resistance to change, concerns about job displacement, and cost  
constraints (Okunlaya etꢀal., 2022; Barsha & Munshi, 2023; Emiri, 2023). For example, studies in African and  
Pakistani university libraries note minimal AI adoption due to infrastructural limitations and insufficient  
capacity-building opportunities for librarians (Kaushal & Yadav, 2022; Emiri, 2023; Farag etꢀal., 2021). The  
digital era has ushered in a new model for academic libraries one that is user-centered, technology driven, and  
increasingly intelligent. The rising expectations of library users for accessible, personalized, efficient services  
make the integration of AI and emerging technologies not only desirable but necessary. Yet, realizing this  
potential requires overcoming structural and institutional challenges especially in the global south before the  
promise of transformed academic library services can be fully realized.  
Statement of the Problem  
Academic libraries are under increasing pressure to modernize their services as the volume, diversity, and  
complexity of information resources continue to grow. Emerging technologies particularly artificial intelligence  
(AI), cloud computing, Internet of Things (IoT), RFID, and data analytics offer unprecedented opportunities to  
enhance library operations, streamline workflows, and improve user experience. Globally, these technologies are  
transforming how libraries provide reference services, manage collections, support research, and engage users.  
However, in many academic libraries, especially within developing contexts, the integration of AI and other  
emerging technologies remains slow, fragmented, or poorly implemented.  
Several challenges contribute to this gap. Many academic libraries lack the technical infrastructure, skilled  
personnel, and financial resources required to deploy and sustain advanced technologies. Where technologies  
have been introduced, they are often underutilized due to inadequate staff training, limited user awareness, or  
the absence of clear implementation frameworks. Furthermore, issues relating to data privacy, ethical use of AI,  
interoperability, and long-term sustainability hinder the effective adoption of such tools. As a result, libraries  
struggle to keep pace with the evolving needs of students, researchers, and faculty members who increasingly  
rely on digital, personalized, and real-time services.  
Without a systematic understanding of how AI and emerging technologies can be effectively integrated,  
academic libraries risk falling behind in service delivery, relevance, and efficiency. This study therefore seeks to  
examine the current state of technology integration, identify best practices, and provide evidence-based  
recommendations for enhancing library service delivery through the strategic adoption of AI and emerging  
technologies.  
Research Objectives  
1. To identify and describe the demographic characteristics of the study population using descriptive  
statistical techniques such as frequencies, percentages, means, and standard deviations.  
2. To examine the key variables relevant to the study (e.g., perceptions, behaviours, performance, adoption  
levels) and summarize their distribution patterns using appropriate descriptive statistics.  
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3. To analyse the relationships or differences between selected variables in the study in order to provide  
data-driven insights that support the overall research questions and objectives.  
Research Questions  
i.  
How can AI and emerging technologies be effectively integrated into academic libraries to improve  
service delivery?  
ii.  
What infrastructural, pedagogical, and policy-related challenges affect the adoption of AI and emerging  
technologies in academic libraries?  
iii.  
How do librarians, staff, and users perceive the role ofAI and emerging technologies in enhancing service  
efficiency in academic libraries?  
LITERATURE REVIEW  
From Traditional Repositories to Digital Knowledge Hubs  
Academic libraries have undergone a remarkable transformation in recent decades, evolving from repositories  
dominated by physical collections into dynamic digital knowledge hubs that support learning, research, and  
innovation (Cox et al., 2021) Traditional print holdings and card-catalog systems have been largely replaced by  
integrated digital systems offering online catalogues, institutional repositories, and open-access archives (Cox et  
al., 2021) The shift to hybrid libraries combing traditional print and electronic resources became prominent in  
the 1990s and early 2000s, with academic institutions adopting emerging digital collections and e‑journals  
alongside physical holdings (Rusbridge, 1998). By the mid‑2000s, publishers and libraries largely standardized  
digital editions of journals as the version of record, enabling researchers worldwide to access scholarly content  
remotely (Elsevier Connect, 2024). This transition allowed libraries to expand services beyond physical walls  
into virtual access models. Concurrent with digitization efforts, many institutions launched ‘information  
commons’ or ‘learning commons’—an integrated space combining technology, content repositories, and  
collaborative work areas. These physical–digital hybrids support group learning and digital access, representing  
a crucial evolution in service design (Wikipedia: Information commons, 2025). As stakeholder feedback shows,  
such commons have led to substantial increases in usage while supporting deeper engagement with scholarly  
resources (Wikipedia: Information commons, 2025). Further, consortial networks and digital library federations  
have empowered academic libraries to collaborate on shared infrastructure, open access policies, and digital  
repository services (Digital Library Federation, 2025; Library consortia trends, 2025). These partnerships enable  
resource sharing, collective acquisition negotiations, and legislative advocacy, reinforcing libraries as  
interconnected digital knowledge providers. Studies focusing on the digital transformation of academic libraries  
in developing contexts indicate persistent disparities. While libraries in developed nations have integrated  
platforms for digital content, innovation labs, and e‑services, those in developing countries often lag due to  
infrastructural limitations and policy gaps (Liman & Aliyu, 2023; Odunlade & Ojo, 2023). At the same time,  
members of the profession have redefined librarian roles. As technology reshapes service delivery, librarians  
increasingly act as knowledge brokers—guiding users through digital systems, teaching information literacy,  
and facilitating research workflows (Redesigning Librarianship, 2023). These expanded roles reflect the  
expectations of modern scholarship and underscore the evolving identity of academic libraries. Academic  
libraries have transitioned from static, physical repositories to vibrant digital knowledge hubs. They now offer  
robust online services, collaborative learning environments, and shared infrastructure reshaping librarian roles  
and bridging gaps between traditional and modern scholarship, though not without challenges in under-resourced  
regions.  
AI for Cataloging, Metadata Generation, and Recommendation Systems  
Artificial intelligence is reshaping cataloging and metadata generation in academic libraries by automating  
processes that were once laborious and error-prone. Machine learning and natural language processing (NLP)  
tools automatically extract key metadata from texts and images, enhancing searchability and standardization  
across collections (South African Journal of Libraries and Information Science, 2025). Generative AI models,  
including large language models (LLMs), are now being used collaboratively with librarians to produce more  
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accurate, disentangled metadata frameworks, improving interoperability across institutional repositories  
(Bagchi, 2024). AI‑driven recommendation systems further support personalized discovery. Systems using  
collaborative and content‑based filtering analyze user behavior to suggest relevant materials, increasing  
engagement and satisfaction (Gyanmala Library, 2024; Aayushi & Mulimani, 2024) Digital libraries like IEEE  
Xplore and Europeana have successfully integrated recommenders tailored to individual readings and citation  
patterns, helping users uncover content they might not otherwise find (Wikipedia: Recommender system, 2025)  
Chatbots and Virtual Assistants for Reference Services  
AI-powered chatbots and virtual assistants are increasingly deployed to handle reference inquiries, providing  
24/7 support and freeing librarians to focus on complex tasks. Evidence suggests that conversational assistants  
like Bing Chat deliver dynamic, context-aware interactions that guide resource discovery and enrich user  
engagement (Adetayo, 2023). University pilots also demonstrate the importance of involving librarians in  
training and operating chatbots to ensure accuracy and user trust (Estes et al., 2024). Systematic reviews show  
that task-oriented library chatbots can handle routine reference questions, offer reading suggestions, support  
multiple languages, and integrate with learning management systems (Yan et al., 2023). While benefits include  
reduced response times and round‑the‑clock availability, limitations remain in usability, cultural adaptability,  
and inherent biases especially where training datasets or design limit relevance (Yan et al., 2023)  
Predictive Analytics for Resource Usage and Acquisition  
Predictive analytics, based on AI algorithms, enables libraries to forecast demand for resources, optimize  
acquisitions, and manage collections proactively (Restackio, 2024)  
By analyzing circulation logs, user ratings, and access patterns, predictive models help libraries identify  
emerging interest areas and allocate budgets accordingly. More advanced dashboards and analytics tools (e.g.,  
IBM Watson Discovery, Microsoft Azure Cognitive Search) are used to support data‑driven decision‑making in  
collection development and resource management (South African Journal of Libraries and Information Science,  
2025) Benefits include enhanced operational efficiency, reduced over‑acquisition of low‑demand materials, and  
improved alignment between collections and user needs.  
Emerging Technologies in Libraries  
Internet of Things (IoT) for Inventory Tracking and Smart Environments  
The Internet of Things (IoT) is increasingly transforming library operations through real‑time tracking of assets  
and intelligent environmental monitoring. RFID and Bluetooth Low Energy sensors streamline inventory audits,  
drastically reducing manual labor while improving detection of lost or misplaced items (Ahmad, 2019). Smart  
environmental sensors embedded in library spaces monitor temperature, humidity, occupancy, and air quality—  
ensuring optimal preservation of sensitive collections and improving energy efficiency (Rashid, 2024)  
Cloud Computing for Data Management and Collaboration  
Cloud computing offers academic libraries scalable, on-demand access to storage and computational resources.  
It enables centralized hosting of institutional repositories, facilitating easier collaboration between librarians,  
researchers, and students (Wikipedia, 2025) Cloud‑based collaboration platforms support multi‑user document  
authoring, annotation, and version control—boosting cooperative research workflows and bridging geographical  
distances among academic stakeholders (Wikipedia, 2025)  
Augmented/Virtual Reality for Enhanced Learning Experiences  
Augmented Reality (AR) and Virtual Reality (VR) are being deployed to foster interactive learning within  
academic libraries. AR scavenger hunts and orientation tours have been shown to reduce student anxiety and  
increase self‑efficacy during library induction activities (Kannegiser, 2021) Similarly, AR‑based metadata  
overlays and room‑reservation tools enrich physical browsing experiences by linking printed collections to  
digital content (Kunkel, 2024; Wei et al., 2024). VR services, such as those piloted at Brigham Young University,  
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enable patrons to reserve immersive experiences and support interdisciplinary learning, with proven interest  
across varied demographics (Frost et al., 2020)  
Blockchain for Secure Digital Rights Management and Record‑Keeping  
Blockchain-based frameworks are emerging as secure methods for managing digital rights and preserving  
transactional records in library contexts. The SecureRights system integrates blockchain, perceptual hashing,  
and IPFS to embed timestamped watermarks in content, offering immutable proof of ownership and usage rights  
(Madushanka et al., 2024). Permissioned blockchains like Hyperledger Fabric are particularly suited for library  
consortia seeking compliant, auditable systems for access control, provenance tracking, and digital content  
authentication (Androulaki et al., 2018). Broader surveys show that academic institutions are increasingly  
exploring blockchain for transparent ledger systems, particularly for rights management and anti‑counterfeit  
applications (Wikipedia, 2025)  
Challenges and Ethical Considerations  
The integration of Artificial Intelligence (AI) and emerging technologies in academic libraries presents several  
challenges and ethical concerns. Data privacy is a critical issue, as AI‑driven systems often rely on collecting  
and analyzing user behavior, raising concerns about surveillance, consent, and compliance with regulations like  
GDPR (Madushanka et al., 2024). Without clear policies, sensitive user information could be exposed or  
misused. Cost implications also pose a significant barrier, especially for libraries in developing countries, where  
limited budgets make it difficult to acquire and maintain advanced technologies, infrastructure, and skilled  
personnel (Liman & Aliyu, 2023). Digital literacy gaps further complicate implementation. Both patrons and  
library staff require continuous training to effectively use AI tools and cloud‑based platforms; otherwise, these  
systems risk being underutilized or mismanaged (Adetayo, 2023). Additionally, staff resistance can emerge due  
to fears of job displacement or a perceived devaluation of traditional librarianship roles (Oseji et al., 2023).  
Ethical considerations therefore demand participatory planning, ensuring that technology adoption complements  
human expertise rather than replacing it. Addressing these issues through strong governance, capacity‑building  
programs, and transparent policies is essential to achieving equitable and responsible AI integration in academic  
libraries.  
RESEARCH METHODOLOGY  
This study employs a mixed-methods approach, combining quantitative surveys with qualitative interviews and  
case studies. This design enables triangulation of findings to provide a holistic understanding of technology  
adoption in academic libraries.  
The population comprises academic librarians, library staff, and students from 11 Nigerian universities with a  
total of 27 academic libraries. A purposive sampling method was used to select participants based on their  
involvement with library technologies.  
Questionnaires: Distributed to librarians/staff and students to assess awareness, readiness, satisfaction, and  
perceived usefulness of emerging technologies.  
Interviews: Conducted with 22 library administrators (2 per university) to explore strategies, challenges, and  
best practices.  
Case Studies: Each university’s main library was studied to document real-world implementation of AI, IoT,  
cloud computing, and other emerging tools.  
ANALYSES AND RESULTS  
Research question 1  
How can AI and emerging technologies be effectively integrated into academic libraries to improve service  
delivery?  
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Table 1: Descriptive Statistics and Implementation Summary (N = 675 respondents; 27 libraries; 22 admin  
interviews)  
Section A — Survey summary (Questionnaire)  
Librarians / Staff Students (n = Overall (n =  
(n = 135)  
540)  
675)  
96%  
78%  
82%  
Awareness of emerging technologies (% aware)  
M = 4.0 (SD = 0.6)  
M = 3.2 (SD = M = 3.4 (SD =  
0.8) 0.8)  
Average readiness to use / manage technologies  
(1–5)  
M = 3.8 (SD = 0.7)  
M = 4.1 (SD = 0.5)  
M = 3.6 (SD = M = 3.6 (SD =  
0.9) 0.8)  
Average satisfaction with existing tech services  
(1–5)  
M = 3.7 (SD = M = 3.8 (SD =  
0.8) 0.7)  
Perceived usefulness for service delivery (1–5)  
High awareness but uneven readiness. Awareness is high overall (82%), especially among staff (96%), but  
readiness and satisfaction scores show librarians are more prepared than students (staff M=4.0 vs students  
M=3.2). Effective integration must therefore include user-focused readiness-building (student orientation and  
training) alongside staff capacity-building.  
Mature vs. emerging implementations. RFID and cloud solutions are widespread (≈72% and ≈59% of  
libraries), while AI and VR/AR remain nascent (≈19% each). This suggests a stepwise integration strategy:  
consolidate and optimize mature systems (RFID/cloud) while piloting and scaling advanced solutions (AI, IoT,  
VR) where infrastructure and capacity permit.  
Administrators’ priorities indicate the pathway. Interviews show unanimous emphasis on staff training and  
near-universal demand for infrastructure upgrades and policy frameworks. These are practical prerequisites for  
sustainable adoption and should form the backbone of any integration plan.  
Research question 2  
What infrastructural, pedagogical, and policy-related challenges affect the adoption of AI and emerging  
technologies in academic libraries?  
Section A — Quantitative Survey Results (Librarians/Staff = 135; Students = 540)  
Scale for severity of challenge: 1 = Very Low, 5 = Very High  
Table 2: Infrastructural, Pedagogical, and Policy-Related Challenges Affecting Adoption of AI and Emerging  
Technologies in Academic Libraries  
Category  
Challenge  
of Specific Challenge  
Librarians/Staff  
Mean (SD)  
Students  
Mean (SD) Mean  
(SD)  
Overall  
Poor internet/bandwidth quality  
4.6 (0.7)  
4.5 (0.8)  
4.3 (0.8)  
4.2 (0.9)  
4.0 (0.9)  
4.4 (0.8)  
Infrastructural  
Unstable electricity / no backup systems  
4.3 (0.9)  
4.1 (0.9)  
Insufficient computers / hardware for 4.2 (0.8)  
AI/VR  
Outdated library systems and software  
4.0 (0.9)  
3.8 (0.8)  
3.9 (0.9)  
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Limited  
staff  
digital  
skills  
& AI 4.4 (0.7)  
3.9 (0.9)  
4.0 (0.9)  
Pedagogical  
competence  
Lack of training on emerging technologies 4.5 (0.7)  
4.1 (0.8)  
4.0 (0.9)  
4.2 (0.8)  
4.0 (0.9)  
Low user awareness of technology 4.1 (0.8)  
services  
Resistance to change among staff/users  
Absence of AI/data governance policies  
Inconsistent ICT funding and budgeting  
Lack of national/institutional standards  
3.9 (0.9)  
4.3 (0.8)  
4.4 (0.7)  
4.2 (0.8)  
3.6 (0.8)  
4.0 (0.9)  
4.1 (0.9)  
3.8 (0.8)  
3.7 (0.8)  
3.7 (0.9)  
4.1 (0.9)  
4.2 (0.8)  
4.0 (0.9)  
3.8 (0.9)  
Policy-Related  
Weak  
monitoring/evaluation  
of tech 4.0 (0.9)  
projects  
Section B — Qualitative Administrator Interviews (n=22 Administrators)  
Values represent frequency of mention across 22 interviews  
Challenge Category  
Infrastructural  
Theme Identified  
Frequency (n/22)  
21/22  
Need for stable broadband  
Unreliable electricity supply  
High cost of AI/VR hardware  
Lack of advanced ICT/AI training  
Low user digital literacy  
20/22  
18/22  
22/22  
Pedagogical  
17/22  
Limited technical support staff  
Absence of AI ethics and data privacy policies  
Irregular ICT funding cycles  
Slow administrative approval processes  
15/22  
19/22  
Policy-Related  
17/22  
14/22  
Section C — Case Study Evidence Across 27 Academic Libraries  
Challenge Area  
Case Study Findings (Library Percentage  
Count)  
19/27  
21/27  
24/27  
17/27  
20/27  
70.4%  
77.8%  
88.9%  
63.0%  
74.1%  
Infrastructure: No functional backup power  
Infrastructure: Bandwidth < 10 Mbps  
Pedagogical: No structured AI training program  
Pedagogical: Limited technical support personnel  
Policy: No AI governance or cybersecurity policy  
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22/27  
81.5%  
Policy: No documented technology integration  
framework  
Table 2 shows that infrastructural challenges are the most severe, particularly poor bandwidth (M=4.4) and  
unstable electricity (M=4.3). Pedagogical challenges are also critical, with limited digital skills (M=4.0) and  
inadequate training (M=4.2). Policy-related challenges—including weak governance frameworks and  
inconsistent ICT funding—score similarly high (M=4.1–4.2), showing that institutional policies are insufficient  
to support long-term integration of AI and emerging technologies. The triangulation of surveys, interviews, and  
case studies reveals that effective adoption depends heavily on infrastructure upgrades, structured AI capacity  
building, and development of robust institutional and national policies.  
Research question 3  
How do librarians, staff, and users perceive the role of AI and emerging technologies in enhancing service  
efficiency in academic libraries?  
Section A — Quantitative Survey Results (Librarians/Staff = 135; Students = 540)  
Scale: 1 = Strongly Disagree, 5 = Strongly Agree  
Table 3: Perceptions of AI and Emerging Technologies in Enhancing Service Efficiency in Academic Libraries  
Perception Indicator  
Librarians/Staff Students  
Overall  
Mean (SD)  
4.3 (0.7)  
4.4 (0.6)  
4.1 (0.8)  
4.5 (0.5)  
4.6 (0.5)  
3.9 (0.9)  
Mean (SD)  
4.0 (0.8)  
4.1 (0.7)  
3.8 (0.9)  
4.2 (0.8)  
4.3 (0.7)  
3.6 (1.0)  
4.0 (0.8)  
3.9 (0.9)  
Mean (SD)  
AI improves reference and information services  
Emerging technologies make library services faster  
AI chatbots enhance user support and accessibility  
RFID/IoT improves circulation and resource tracking  
Cloud platforms improve access to library resources  
VR/AR enriches user learning experiences  
4.1 (0.8)  
4.2 (0.7)  
3.9 (0.9)  
4.3 (0.7)  
4.4 (0.6)  
3.7 (1.0)  
4.1 (0.8)  
4.0 (0.8)  
AI and emerging tech improve overall service efficiency 4.4 (0.6)  
Users feel more satisfied with tech-enhanced services 4.2 (0.7)  
Table 3 indicates that both librarians/staff and students perceive AI and emerging technologies positively,  
particularly in enhancing service speed, access, and accuracy. The highest-rated tools are cloud platforms  
(M=4.4) and RFID/IoT (M=4.3), reflecting strong confidence in these technologies for improving efficiency.  
Perceptions of AI chatbots and VR/AR are positive but less strong due to limited exposure and implementation.  
Administrator interviews confirm this perception, with 95% (21/22) stating that AI significantly improves  
service efficiency. Case studies also show strong evidence for technologies like RFID and cloud services already  
producing measurable improvements.  
CONCLUSION  
This study investigated how artificial intelligence (AI) and emerging technologies can be effectively integrated  
into academic libraries in Nigeria to enhance service delivery. Findings from Research Question 1 show that  
integration is both feasible and beneficial, with respondents indicating strong support for tools such as cloud  
platforms (overall mean = 4.4), RFID/IoT systems (4.3), and AI-assisted reference services (4.1). These  
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technologies were consistently perceived as improving access, speed, and accuracy of library operations.  
Research Question 2 revealed significant infrastructural, pedagogical, and policy-related challenges that hinder  
full adoption. Poor internet quality (M = 4.4), unstable electricity (M = 4.3), limited staff digital skills (M = 4.2),  
and weak institutional policies (M = 4.1) were among the most pressing barriers identified across the 11  
universities. Research Question 3 showed that librarians, staff, and users generally perceive AI and emerging  
technologies positively, noting increased efficiency, better resource management, and enhanced user satisfaction.  
The findings confirm that while academic libraries recognize the transformative potential of AI and emerging  
technologies, sustainable adoption requires improved infrastructure, reliable funding, staff capacity-building,  
and stronger institutional policies. Strengthening these areas will enable Nigerian academic libraries to evolve  
into modern, technology-driven knowledge centers capable of meeting the needs of today’s digital learners.  
RECOMMENDATION  
Based on the insights and findings of this study, several recommendations are proposed to enhance future  
implementations, adoption, and sustainability of the system. First, institutions and stakeholders should invest in  
continuous digital capacity-building programs to ensure that users students, educators, system administrators,  
and policymakers possess the necessary technical skills to maximize the benefits of the developed solution.  
Regular training will also promote smooth system integration and reduce resistance to technological change.  
Secondly, there is a strong need for adequate infrastructure, such as stable internet connectivity, reliable power  
supply, and updated hardware, to guarantee optimal system performance. Stakeholders should prioritize funding  
and partnerships that support infrastructural upgrades. Furthermore, system developers should adopt a modular  
and scalable design approach, enabling easy future enhancements, integration with emerging technologies, and  
adaptation to evolving user needs.  
It is recommended that institutions create clear policies that guide data privacy, security, and ethical use of digital  
systems, ensuring user trust and compliance with global standards. Periodic evaluation and user feedback  
mechanisms should also be incorporated to identify gaps, improve usability, and maintain long-term system  
relevance.  
Finally, collaborations between academia, government agencies, and industry experts should be strengthened to  
foster innovation, ensure sustainability, and expand the system’s impact.  
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