Adoption of artificial intelligence technologies among librarians in higher education libraries: systematic review

INTRODUCTION

Artificial intelligence (AI) has evolved in recent decades, primarily in response to growing demands for efficiency, large-scale data processing, personalised services, and automated decision-making across sectors.(1,2,3) These needs have driven the development of AI systems capable of performing tasks such as natural language processing, predictive analytics, image recognition, recommender functions, and automated classification, capabilities that directly align with the operational requirements of libraries. As AI systems advanced, libraries, particularly those in higher education, began adopting them to enhance discovery, streamline technical processes, and improve user engagement.

In the library domain, AI has been applied in diverse ways. Early systems focused on automating cataloguing, metadata extraction, and indexing, but more recent applications include conversational chatbots for reference services,(4) plagiarism-detection platforms, intelligent discovery layers,(5) recommender systems,(6) and analytics-driven decision support tools.(7) These technologies have emerged in direct response to libraries’ increasing workload, the complexity of digital collections, and user expectations for personalised, real-time services.

A growing body of studies has examined the use of AI systems among librarians. Research from North America shows early adoption of AI-enabled discovery, chatbots, and automated plagiarism detection.(4,8) Studies in Asia reveal selective but expanding applications, often driven by institutional investments in digital transformation.(9,10,11) Work from African contexts highlights growing awareness but limited adoption due to infrastructural and skills-related constraints.(6,12,13) Most importantly, findings across these regions consistently point to challenges relating to competencies, policy preparedness, and uneven adoption paths.

Despite this expanding literature, gaps remain in synthesizing what types of AI technologies are actually available to librarians, what competencies they possess to use them, how extensively these systems have been adopted in higher education libraries, and the persistent challenges that hinder effective integration. Existing studies tend to focus either on perceptions, awareness, or specific applications without offering a consolidated understanding of the broader adoption. Thus, there is a need for a systematic review that brings together evidence across regions and contexts.

This study, therefore, provides a comprehensive synthesis of AI adoption among librarians in higher education libraries, focusing on the technologies used, competencies required, extent of adoption, and existing challenges. By consolidating current knowledge, the study offers insights that can support policy development, capacity building, and strategic planning for AI integration in academic libraries. Therefore, the need to explore the adoption of AI technologies by librarians in higher education libraries becomes necessary at this time. Hence, this paper aims to review and synthesize existing evidence on the adoption of artificial intelligence technologies in higher-education libraries, focusing on the types of AI systems used, the competencies of librarians, the extent of adoption, and the challenges affecting implementation.

METHODS

This study adopted a systematic literature review (SLR) approach guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. PRISMA offers a structured approach for reporting evidence-based studies by following four stages, including identification, screening, eligibility, and inclusion. Its rigor has made it applicable in information and library science research. (14) The choice of SLR is appropriate because the study seeks to synthesize empirical and conceptual evidence on the intersection of AI technologies, librarian competencies, and adoption in higher education institutions.

The literature search focused on English-language, peer-reviewed articles and conference papers published between 2019 and 2025. This timeframe was chosen to capture the most recent discourse on AI adoption in libraries, given the surge of interest in AI applications during and after the COVID-19 pandemic. The databases searched included Scopus, LISA, Web of Science, and Google Scholar, selected for their strong coverage of peer-reviewed publications in library and information science. Search terms were developed to reflect the study (AI technologies, competencies, and adoption) and their application in higher education libraries. The following combinations of keywords were applied: For Scopus, LISA and Google Scholar databases: TITLE-ABS-KEY ("artificial intelligence" OR "AI technologies" OR "chatbot*" OR "machine learning" OR "robotics" OR "expert system*" OR "natural language processing" OR "recommender system*" OR "large language model*" OR "ChatGPT" OR "Gemini") AND (Librar* OR "academic librar*" OR "higher education librar*" OR "university librar*") AND (competenc* OR skill* OR adoption OR awareness OR perception*). For Web of Science database, the search keywords are: TS ("artificial intelligence" OR "AI technologies" OR "chatbot*" OR "machine learning" OR "robotics" OR "expert system*" OR "natural language processing" OR "recommender system*" OR "large language model*" OR "ChatGPT" OR "Gemini") AND (Librar* OR "academic librar*" OR "higher education librar*" OR "university librar*") AND (competenc* OR skill* OR adoption OR awareness OR perception*). These search terms were broad enough to capture literature on AI applications in academic libraries while being specific to competencies and adoption in higher education contexts. Filters were applied to document type (journal articles and conference proceedings) and to the field of Library and Information Science.

The initial search yielded 472 journal articles and conference papers, which were exported to an Excel spreadsheet for management. Duplicate and triplicate records were removed, resulting in 334 articles. Titles and abstracts were screened for relevance, reducing the number to 141 articles for full-text review. During the eligibility stage, articles not related to academic libraries, librarians’ competencies, or AI adoption were excluded. Specifically, 78 articles were excluded because they focused on general ICT applications or AI outside the context of higher education libraries. Fifteen (15) articles could not be retrieved in full text and were also excluded. At the conclusion of the process, 48 articles met all the criteria and were included for analysis and synthesis (see Figure 1 for PRISMA flow diagram). (15)

Source: Author’s work

Data from the included studies were extracted into an Excel sheet. The synthesis process involved thematic analysis, grouping findings according to the study objectives: identification of AI technologies, assessment of competencies, extent of adoption, barriers to adoption, and strategies for improvement. To ensure validity and accuracy, both researchers were involved in each stage of the process, such as searching, screening, eligibility, and inclusion. Each stage of screening was repeated three times over a week to minimize bias and errors. Cross-checking was also performed to validate the consistency of the extracted data.

Research Characteristics

The study comprises 48 articles published between 2023 and 2025. Holistically, this study consists of 46 peer-reviewed journals and 2 conference papers. In addition, the graphical distribution of articles by year of publication reveals a steady growth in scholarly contributions on artificial intelligence in academic libraries between 2019 and 2025 (Figure 2). In the years between 2019 and 2020, the number of publications was relatively low, reflecting the emergent stage of interest in AI within the library and information science field. A noticeable increase is observed beginning in 2021, suggesting that the COVID-19 pandemic and the subsequent acceleration of digital transformation may have contributed to growing research interest in AI applications in libraries. By 2022 and 2023, the number of articles significantly rose, indicating that researchers were increasingly exploring the implications, opportunities, and challenges posed by these technologies in academic library contexts.

Source: Author’s Work

The distribution of articles across journals and conferences, as shown, reveals an interesting pattern about where most of the scholarly contributions in the literature are concentrated. A closer look at the chart shows that while several journals hosted one article, a few journals and conference outlets appeared more, indicating a clustering of research dissemination in particular outlets (Figure 3). Given available data, journals such as The Journal of Academic Librarianship, Information Development, Open Information Science, and New Review of Academic Librarianship emerged as some of the most common platforms for publishing on the subject. These outlets accounted for more than one contribution each, suggesting that they serve as preferred or popular venues for scholarship in artificial intelligence applications within libraries. On the other hand, a wide range of other journals such as Business Information Review, Journal of Information Literacy, Library Hi Tech, The Reference Librarian, and others featured only one article each, highlighting the broad spread of interest in the subject across the information science field.

Source: Author’s Work

RESULTS

Types of AI technologies available and used by librarians in higher education institutions

The finding identifies a variety of AI technologies that are either available for library use or are already being applied in higher education institutions. These technologies cut across user services, technical services, analytics, and instructional support, reflecting the diverse roles of libraries. One of the most widely reported AI applications in libraries is the use of chatbots and virtual assistants. These technologies are employed to provide round-the-clock reference services, answer asked questions, and guide users in navigating library resources. Studies in Nigeria and Pakistan report that librarians are adopting chatbots to improve user support and extend services beyond staffed hours. (9,12) Similarly, a study documents their use in Canada and the United States, where librarians highlight chatbots as a leading example of AI adoption. Another significant category of AI technologies available to and used by librarians includes AI-enabled search and recommendation systems. (4) These technologies use natural language processing and machine learning to improve resource discovery, provide personalised recommendations, and enhance retrieval accuracy. Michalak and Ellixson (5) note that AI discovery layers embedded in library platforms are increasingly adopted to enrich user experiences. Studies similarly report that academic libraries use AI-driven recommendation engines to tailor content to students and researchers. (6,16)

In addition, the automation of cataloguing and metadata generation is another area where AI technologies are being applied. Machine learning algorithms and natural language processing technologies are used to extract metadata from digital collections, classify large corpora, and generate subject headings. Ali et al. (17) observe that librarians rely on such AI technologies to enhance cataloguing efficiency, while Ahmad et al. (7) identify competencies in handling big data analytics for library operations. Michalak and Ellixson (5) also point to AI applications in metadata correction and enrichment, which librarians supervise for accuracy. Also, plagiarism detection technologies such as Turnitin, iThenticate, and other similarity-checking software are adopted AI-based technologies in academic libraries. These are used not only by faculty and students but also by librarians as part of research support services. Studies highlight librarians’ role in teaching users to interpret the results of such technologies and integrate them into academic integrity training. (18,19) Hervieux and Wheatley (4) also document adoption in North American libraries, where plagiarism detection is a routine service supported by librarians.

The literature also identifies technologies that directly support research and learning, including automated citation managers, AI-driven summarisation technologies, and generative AI writing assistants. Librarians are not only users of these technologies but also guide students and researchers in their ethical and effective use. (20,21,22,23,24) Studies librarians encourage the use of AI-supported learning technologies, but stress the need for critical evaluation to avoid overreliance. Further, AI is increasingly used in analytics for decision-making in collection development, usage trend analysis, and service improvement. Ahmad et al. (7) highlight librarians’ involvement in big data analytics for improving service delivery. Literature document the use of AI-based analytics in understanding user behaviour, predicting resource demand, and informing strategic planning. (15,25,26,27,28,29) These technologies are often embedded in library dashboards or provided by vendors, enabling librarians to make data-driven decisions. Further, several studies report the use of AI-powered Optical Character Recognition (OCR) and semantic indexing technologies to digitise and process large archival collections. These technologies are particularly valuable in libraries digitising legacy collections and making them machine-readable. (25,26,27,28,29) Also, AI-based text mining and entity extraction are used in research support to aid systematic reviews and knowledge discovery. (3,17,30,31,32,33,34)

Competencies librarians possess in the use of AI technologies for library service delivery

The literature identifies a range of competencies that librarians currently possess or are developing in relation to artificial intelligence (AI) for service delivery. Several studies show that librarians are equipped with basic digital literacy and data-handling skills that form the foundation for AI adoption. These include proficiency in using library management systems, bibliometric technologies, and basic data analysis platforms, which allow librarians to interpret outputs from AI-driven systems. Some studies highlight competencies in big data analytics interpretation, while others report librarians’ ability to work with information technology technologies in the digital era as a precursor to AI use. (22,35,36,37,38,39,40) These foundational skills are widespread but not yet advanced enough for full-scale AI development.

One of the clearest competencies lies in metadata curation, classification, and cataloguing. Studies reveal that librarians possess strong skills in evaluating and refining AI-automated metadata, which ensures the accuracy of cataloguing and indexing. It was noted that librarians are adept at applying domain-specific standards to improve the results of AI-generated metadata.(25,34,41,42,43) This shows librarians’ competencies in acting as human supervisors of machine-driven processes. Another major competency is the ability to integrate AI into teaching and learning. Librarians are already skilled in delivering information literacy training, and this extends to guiding users in the ethical and effective use of AI technologies. Studies stress librarians’ capacity to embed AI literacy into curricula, teaching students how to critically assess AI outputs, avoid misinformation, and use AI for research support. (22,28,36,44,45,46) This pedagogical competence is one of the strongest across contexts, positioning librarians as educators in AI literacy.

The literature consistently identifies librarians’ ability to evaluate AI technologies through professional judgement and ethical awareness. It was emphasized that competencies in understanding policies, data ethics, and AI literacy guidelines for libraries.(20,23,25,47) Similarly, some scholars state librarians are competent at reflecting on the risks, biases, and ethical dilemmas of AI integration in library practice.(20,47,48,49) Many librarians demonstrate competence in configuring and managing AI-enabled user-facing systems, such as chatbots, recommendation systems, and plagiarism checkers. Findings report that librarians are able to deploy AI applications in user services. (12,32,40,41) These competencies are typically less technical and more operational, but they allow librarians to deliver effective front-line AI-enabled services. Finally, the literature points to librarians’ competencies in managing projects, collaborating with IT professionals, and aligning AI use with institutional goals. Studies stress librarians’ abilities to strategize around AI adoption, even if they lack coding skills.(22,25,34,48) Competence here lies not in building systems but in integrating AI projects into broader library operations and liaising with other professionals for successful implementation.

Extend of adoption of AI technologies in libraries of higher education institutions

The findings reported that while awareness of artificial intelligence (AI) in academic libraries is high, the actual extent of adoption remains relatively limited and uneven across institutions and regions. For instance, empirical studies in Nigeria and other African contexts reveal that most librarians acknowledge the importance of AI but report low levels of implementation, with AI often confined to experimental projects or policy discussions rather than widespread operational use.(12,16,50,51,52) Similarly, studies indicate that adoption is still in its infancy, constrained largely by infrastructural and financial limitations.(15,33,53,54)

In contrast, studies from Asia, particularly in Pakistan, India, and Turkey, show a growing but selective adoption of AI, with applications concentrated in specific service areas such as cataloguing automation, discovery services, and chatbots for user support. Librarians in these settings often report that AI has not yet been fully integrated into core library operations, but isolated deployments demonstrate significant potential.(17,20,25,55,) A similar pattern emerges where librarians demonstrate awareness of ethical issues and potential applications, but practical adoption is still relatively low and uneven across institutions.(22,56,57)

Findings from North America and Europe suggest a higher degree of adoption, though still uneven and often mediated by vendor solutions. For example, surveys of librarians in Canada and the United States highlight the use of AI-driven discovery layers, plagiarism detection software, and chatbots, but emphasize that adoption is piecemeal rather than systematic across institutions.(4,58) Across contexts, the adoption of AI is more advanced in user-facing applications (such as virtual assistants, recommendation systems, and plagiarism checkers) than in back-end functions like advanced analytics or metadata automation. Most studies stress that institutional capacity, availability of training, and policy frameworks strongly influence the extent to which AI technologies move from discussion to active deployment.(51,52) Where AI has been adopted, it is often vendor-driven, with libraries relying on commercial discovery platforms, rather than internally developed or locally adapted solutions.(23,44,55,58)

Challenges hindering the effective adoption of AI technologies among librarians in higher education institutions

Findings revealed that infrastructure and cost constraints hinder effective adoption of librarians in the use of AI technologies. Many studies from lower- and middle-income contexts emphasise that libraries lack reliable internet bandwidth, modern servers, and budgetary headroom for licences or custom development; as a result, AI projects often stay at pilot stage or rely on vendor-hosted solutions that the institution cannot sustain long-term.(12,51) Closely linked is a pervasive skills and training deficit. Across surveys and reviews, librarians report conceptual awareness of AI but limited hands-on skills in data science, model evaluation, or systems integration; most feel more comfortable teaching about AI or configuring vendor technologies than developing or maintaining AI systems themselves. (58)

The third major challenge is policy, governance, and ethical concerns. Authors highlight uncertainty about acceptable uses, data privacy, bias, and accountability for AI outputs, concerns that slow adoption because institutions hesitate to deploy technologies without clear policies or guidance.(17,19) Ethical literacy and governance frameworks are often absent or embryonic; where they do exist, they substantially accelerate responsible adoption, but many libraries report waiting for university-level policy or national guidance before scaling solutions. In addition, a strategic and organisational obstacle is the buy-versus-build dilemma and vendor dependency. Several studies note that many libraries rely on commercial discovery platforms and vendor-supplied chatbots, which reduces the need for in-house technical skill but increases lock-in, recurring costs, and reduced control over data and customization. (16,22)

Data quality, interoperability, and legacy systems are also frequent technical blockers. Libraries’ existing catalogues, digitised collections, and institutional data often require cleaning, standardisation, and enrichment before AI-driven analytics or NLP can work reliably; integration with legacy ILS and authentication systems further complicates deployment. (19,46,52) Finally, cultural resistance, professional anxiety, and competing priorities slow adoption. Several papers document librarian anxieties about job displacement, concerns about the quality of AI-generated content, and a preference for focusing scarce time on core services. (4)

CONCLUSIONS

This systematic review set out to examine how artificial intelligence is being adopted in higher education libraries and to bring together existing evidence on the technologies in use, the competencies needed by librarians, the degree of adoption, and the factors that influence implementation. The review shows that artificial intelligence is becoming an important part of library practice as institutions seek better ways to manage information resources, support users, and improve service delivery. Although specific technologies and levels of readiness vary across regions, there is clear movement toward more technology assisted library operations.

The studies reviewed make it clear that successful adoption of artificial intelligence depends on more than the availability of tools. Librarians require continuous learning opportunities, supportive institutional policies, and access to reliable infrastructure. Where these conditions are present, artificial intelligence is more likely to be used in meaningful and sustainable ways. Where they are lacking, adoption remains slow and limited. The broader implication of the review is that librarians continue to play a central role in guiding the responsible and effective use of artificial intelligence. Their expertise in information management, instruction, and ethical practice remains essential as libraries integrate new technologies. Continued investment in professional development, policy development, and collaboration across departments will be necessary for libraries to benefit from artificial intelligence in a responsible and well-coordinated manner.

However, the study has some limitations. First, the study relied primarily on secondary data from published literature, which may not fully capture emerging practices or context-specific innovations in different regions. This reliance also means that the findings are shaped by the scope, depth, and methodological rigor of the reviewed studies, which varied considerably. Second, the study did not include empirical field data such as surveys or interviews with practicing librarians, which could have provided richer and more context-specific evidence on real-world practices. Finally, the rapidly evolving nature of AI technologies means that some findings may quickly become outdated as new technologies and competencies emerge. Future research should address these limitations by conducting empirical investigations that capture librarians lived experiences, adoption strategies, and user feedback across different institutional contexts. Comparative studies between developed and developing regions, as well as longitudinal studies tracking the evolution of AI competencies and adoption in libraries over time, would also provide deeper insights to strengthen practice and policy.

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