Staff Training and the Use of Artificial Intelligence (AI) in University Libraries

1. Introduction

Artificial intelligence (AI) has transformed technology worldwide, impacting each facet of development, including libraries. Libraries are the hub of any educational establishment and are a resource where people of all demographic backgrounds can obtain information. University Libraries in the university environment are primarily intended to give their communities access to knowledge-based material and other scholarly resources to assist their parent institutions’ overall educational goals (Okpokwasili, 2019). One of the modern digital revolutions is artificial intelligence (AI), which university libraries can use to their fullest potential to provide users with a diversity of more expedient library services. Libraries, a sector tasked with giving people the information they need to be current, relevant, and thriving in this AI-driven era, must incorporate AI due to its rapidly expanding ramifications in our daily lives.

Artificial intelligence (AI) is the capability of machines to perform as human intelligence. Benhamou and Janin (2018) defined AI as a group of knowledge that allows machines to behave with an extraordinary degree of intellect, much like humans. Artificial intelligence (AI) is another breakthrough that cleverly employs machines to accomplish tasks that individuals can do and perform faster than individuals, such as processing enormous amounts of data and making forecasts (Wheatley & Hervieux, 2019). AI is a force that redefines human intellectual, social, and professional landscapes; it is more than just a technology phenomenon (Redek, Marčič, Maldonado, da Silva, & Shenstone, 2023). For instance, academic libraries in emerging nations like Nigeria have not utilised AI as much as they might. According to Wheatley and Hervieux (2019), libraries in many emerging economies have not embraced digital technology, and they have demonstrated resistance to modifications in how technologies are used in various library functions. Tella (2020) argued that although libraries in developing nations are still gaining traction, those in advanced countries have embraced and employed AI technologies in almost every aspect of society. However, there is a need for library staff to be trained to use AI effectively in university libraries.

Staff training is an organisational technique that involves investing in personnel to foster the knowledge, skills, talents, and values that will ultimately improve workers’ job performance and boost productivity within the company (Bakare, Akinyemi, & Quadri, 2023). Training is a method of improving employee skills to attain the anticipated goals (Ogundele, 2019). Bakare (2023) opined that given the evolving information demands of library patrons, library staff members must get ongoing training to acquire the new competencies necessary for efficient service delivery in the twenty-first century. Adequate training is essential for library personnel to attain the skills and knowledge to use AI technologies effectively. Moreover, Ajulo and Achonna (2020) stressed that one of the most important strategies for ensuring that librarians can use ICT to address challenges in libraries and information services is to train and retrain them in ICT applications. In this digital age, training helps librarians increase productivity and meet the information needs of library users. Furthermore, training prepares librarians to identify, analyse, and make critical decisions that will improve their productivity in the library. According to Akinyemi and Afolabi (2023), one of the issues with ICT utilisation in libraries is the inappropriate internet training and ICT literacy. Likewise, Smith (2023) posited that AI is better used in a library that prioritises staff training. Institutions that invest in comprehensive staff training improve AI readiness and foster an innovative and adaptable culture (Johnson, 2021).

Several studies have examined the integration of AI into service delivery in university libraries, and they emphasised on the need for library personnel to be trained to enable them using AI for library operations (Walker & Jiang, 2019; Abayomi et al., 2021; Liu et al., 2022; Lo, 2024; Yusuf et al., 2022). However, this study is novel as it explores the required training for library personnel in using AI in university libraries. This training includes introductory training on AI fundamentals, programming and coding, conferences and workshops on AI, and ethical considerations of AI. Librarians must know AI concepts like machine learning and natural language processing. This will help them understand how AI tools work and their potential applications in the library. Jones and Brown (2022) opined that AI in libraries necessitates specialised capabilities such as data analytics, machine learning techniques, and natural language processing. Moreso, training in programming and coding is essential for librarians in the cataloguing unit. For example, librarians can learn to use AI-powered chatbots to assist patrons or AI-driven systems for personalised recommendations. AI training for librarians should also cover ethical considerations such as data handling and data privacy, copyright, and bias in AI systems. This will guarantee the accountable and equitable use of AI technologies in libraries.

Furthermore, conferences and workshops on AI will help librarians to stay current on new tools and trends. Thus, this will contribute to the skill development necessary for librarians to harness the potential of AI technologies effectively. Whittlestone et al. (2020) discussed that workshops, webinars, and collaborative learning experiences enhance librarian awareness of AI. Other training on specific AI tools relevant to library operations, including user experience (UX) design, AI evaluation and assessment, etc. AI is crucial in university libraries to meet the users’ information needs in this digital age. Therefore, the staff of university libraries must acquire new skills through training to stay competent in using AI to provide better services to users. The study aimed to determine the required training for library staff in using AI in university libraries and the advantages of using AI in university libraries. Despite the growing interest in incorporating AI into the operations of academic libraries, notably in advanced nations, there is still a significant gap in the staff training tailored to this technological transition in university libraries, particularly in Nigeria. Based on this gap, this study investigated staff training and the use of AI in university libraries in South-West Nigeria.

2. Literature Review

3. Methodology

The research employed a survey design. The study population was 91, comprising all the librarians (professional librarians) working in nine selected university libraries in Ondo, Osun, and Ekiti States, Southwest Nigeria. These states were used due to proximity. The universities were selected based on Federal, State, and Private institutions in each respective state. A census sampling was employed for the study. The entire population served as the study’s sample size due to the small and controllable population size. Lawrence (2019) asserted that using the total population is allowed when a population is under control. An online Google Form questionnaire was used to elicit data from the participants. The instrument consisted of sections A and B. Section A consisted of five questions focusing on the demographic attributes of the respondents. Section B consisted of fifteen questions on staff training and the use of AI among university librarians. The questions in section B were in the form of four Likert scales in closed-ended questions. The four-point Likert scale allowed the respondents to take a definite position about their training needs and experience with AI. The study was analysed with Statistical Package for Social Sciences (SPSS) version 23 using descriptive and inferential statistics such as frequencies, percentages, mean and standard deviation. Pearson Product-Moment Correlation Analysis was used to analyse the relationship between staff training and the use of AI in university libraries at a 0.05 level of significance. The criterion mean was placed at 2.50, indicating that the average mean below 2.50 is considered low and the average mean above 2.50 is considered high.

4. Results

Table 1 revealed that 72 responses were retrieved from 91 librarians, indicating a 79.1% response rate. The response rate of 79.1% is appropriate because 60% is the minimum standard response rate set by Clearinghouse (2019) in social science and education. The highest responses (16.48%) were received from librarians in Obafemi Awolowo University, Ile-Ife, followed by those from Afe Babalola University, Ado-Ekiti (13.89%), Ekiti State University, Ado-Ekiti (9.89%), and Osun State University, Ado-Ekiti (9.98%). However, the fewest responses came from Elizade University, Ilara-Mokin (4.40%). This is because they have only 5 librarians, and not all of them answered the questionnaire, like that of Joseph Ayo Babalola University, Arakeji.

Table 1.

Distribution of Librarians and Rate of Responses from the Sampled Universities

Table 2 revealed the demographic distribution of respondents. Responses on gender revealed that 59.7% (43) were female, while 40.3 % (29) were male. Responses on marital status showed that among the librarians, 79.17% (57) were married, and 20.83% (15) were single. Of the designation, there were University Librarians 5.5% (4), Deputy Librarians 8.3% (6), Principal Librarians 13.9% (10), Senior Librarians 19.4% (14), Librarian I 33.3% (24), Librarian II 15.3% (11), Assistant Librarians 4.2% (3). This indicates that there were more professionals in the cadre of Librarian I than in other cadres in the university libraries, followed by Senior Librarians and the least is Assistant Librarians. Responses on the qualification showed that about 35% (25) had PhD degrees, while about 65.3% (47) were holders of MLIS. This indicates that most of the librarians hold an MLIS degree. More study participants were working in units such as Characteristics Frequency Percentage % Gender: Female 43 59.7 M ale 29 40.3 Total 100 Marital Status: Married 57 79.17 Single 15 20.83 Total 100 Designation University Librarian 4 5.5 Deputy University Librarian 6 8.3 Principal Librarian 10 13.9 Senior Librarian 14 19.4 Librarian I 24 33.3 Librarian II 11 15.3 Assistant Librarian 3 4.2 Total 100 Academic Qualification PhD 25 34.7 MLIS 47 65.3 Total 100 Unit/Department Cataloguing and Classification 33 45.8 Acquisitions 10 13.9 Circulation 15 20.8 Reference 8 11.1 Serials 6 8.3 Total 100 Cataloguing and Classification 43.8% (33), Circulation 20.8% (15), and Acquisitions 13.9% (10).

Table 2.

Demographic characteristics of the respondents

• ∙ Research Question 1. What is the required training for library staff in using AI in university libraries?

Table 3 reveals the mean and standard deviation of the required training for library staff using AI in university libraries. The grand mean is (x =2.99) higher than the criterion mean (x =2.50). Training on the ethical considerations of AI use in libraries and training in programming and coding have equal and highest mean (x =3.26). Other training with high mean includes training on data handling and data privacy (x =3.18), introductory training on AI fundamentals (x =3.11), and conferences and workshops on artificial intelligence (x =2.99). Training in design and user experience (x =2.58), and training on AI evaluation and assessment (x =2.57). Since all the variables have a high mean, it denotes that training is highly needed for library staff to enable them to use artificial intelligence in university libraries.

Table 3.

The Mean and Standard Deviation of the Required Training for Library Staff in Using AI in University Libraries

• ∙ Research Question 2. What are the Advantages of Using AI in University Libraries?

Table 4 reveals the mean and standard deviation of the advantages of using artificial intelligence (AI) in university libraries. The grand mean is (x =2.98) higher than the criterion mean (x =2.50). Artificial intelligence facilitates rapid information dissemination and retrieval and has the highest mean (x =3.46). This is followed by using AI to enhance the effectiveness of library service delivery, and using AI in university libraries lessens librarians’ stress and workload (x =3.18) and (x =3.17), respectively. AI tools help librarians automate repetitive processes like indexing and cataloguing (x =3.08). “Using AI in the library enhances the security and precision of library data” and “AI relieves librarians of manual and repetitive tasks” scored the same mean (x =2.94). AI enables staff to respond to user inquiries promptly (x =2.78). All the variables have a higher mean than the criterion mean, except “using AI in the library helps to optimise resource allocation and manage library inventories more efficiently,” which scored a mean (x =2.26) less than the criterion mean. The low mean may be due to a lack of AI expertise because the allocation of library resources is sensitive among library functions. It was revealed from this analysis that the use of AI in the university library enhances library operations.

Table 4.

The Mean and Standard Deviation of the Advantages of Using Artificial Intelligence (AI) in University Libraries

• ∙ Hypothesis

There is no significant relationship between staff training and the use of AI in university libraries.

Table 5 reveals the Pearson Product-Moment Correlation Coefficient r = 0.887, and the significant value (Sig.2-tailed) is 0.000 (less than 0.05). This shows a relationship between staff training and the use of AI in university libraries in southwest Nigeria. The null hypothesis is, therefore, rejected, but the alternative is accepted. This means that training on AI enables the librarians in the university libraries to use AI effectively in library operations.

Table 5.

The relationship between staff training and the use of AI in university libraries

5. Discussion of findings

Table 3 answered the questions on the required training for library staff in using AI in university libraries. The findings revealed that AI training, such as introductory training on AI fundamentals, Programming and Coding, evaluation of AI, Conferences and Workshops on AI, and ethical considerations of AI, among others, is highly required for artificial intelligence in university libraries. This finding agrees with Floridi and Taddeo’s (2023) findings that librarians are more prepared to address the ethical implications of AI, such as concerns about privacy, bias, and transparency, when they receive staff training that incorporates ethics training. Additionally, the findings align with Parry and McCarthy’s (2023) position that staff members can more efficiently install and manage AI tools in libraries after participating in training programs. Likewise, the study by Johnson (2024) affirmed that training programs that support creativity and innovation empower librarians to investigate novel approaches to incorporating AI into library services. Similarly, Jones and Brown (2022) opined that training programs that equip library staff with ICT competencies are essential.

In addition, Table 4 shows the advantages of using AI in university libraries. It was found that the benefits of using AI in university libraries were high. This includes that AI helps the staff to respond to user inquiries promptly, facilitates rapid information retrieval, relieves librarians of manual and repetitive tasks, enhances the effectiveness of library service delivery, and AI in university libraries will lessen librarians’ stress and workload, among others. This finding agrees with Li’s (2021) study on library network security with the integration of AI. The findings show that AI can enhance the security and protection of library management networks. Similarly, Hussain’s (2023) research showed that AI is a powerful tool that may improve library services. Akinyemi (2023) also agrees that AI improves inventory control and resource allocation, also known as resource management. Moreover, the study discovered a significant relationship between staff training and AI use in university libraries in southwest Nigeria. Likewise, Walker and Carbery’s (2024) study asserted that when given the required training, library employees can use AI to do all the labour-intensive jobs like cataloguing, indexing, and reference management.

Furthermore, the findings of this study agree with scholars on the relationship between training and employees’ job performance in an organisation. For instance, the findings of a study by Daniel (2019) on the effects of human capital development on organisational performance established a connection between training and organisational performance. The result showed that training enhances employees’ skills and knowledge in addressing the obstacles posed by technological innovation. Akinyemi and Afolabi’s (2023) research on the use of ICT in public polytechnic libraries in South-West, Nigeria, found that the use of ICT is low due to the lack of ICT skills. They suggested that library personnel should be trained to acquire ICT skills. Similarly, Mercy and Wali (2021) investigated the influence of staff training and development on librarians’ job performance in federal university libraries in South-South Nigeria. The result also showed that training and development programmes influence librarians’ job performance. Based on the findings, the study recommended staff training and development to be one of the regular exercises by university libraries in South-South Nigeria.

6. Theoretical Framework Theory of Human Capital

Human Capital Theory was used to underpin the findings of this study. The theory of human capital was initially proposed by Schultz (1961) and further refined by Greg Becker in 1964. Human Capital Theory illustrates how an organisation can increase employee capacity by accumulating knowledge, skills, and abilities through training and development. The notion claims that training increases employee productivity by providing information, skills, and talents (Abdul-Rasheed & Adebayo, 2018). In other words, human capital includes methods for developing human characteristics that can improve organisational productivity. The human capital theory proposes that individuals invest in themselves. The theory explains the significance of labour maximisation and how an organisation might gather employees’ knowledge, skills, and abilities to build employee capability and increase organisational performance. Skill denotes the capability to apply information and techniques to fulfil responsibilities and unravel issues. Knowledge is an employee’s intellectual grasp of how to increase organisational performance. Ability is the physical capacity of an employee to perform and accomplish work. It refers to the attribute or state of being able or capable of performing something.

This theory aligns with this study because the theory illustrates how an organisation can increase employee capacity by accumulating knowledge, skills, and abilities through training and development. In this new digital era, librarians in university libraries are expected to deliver library services to the best of their capabilities and meet users’ information needs with AI. For the librarians in the university libraries to be competent in AI and effectively use AI to deliver library services, they need to acquire some skills and knowledge on AI that will enable them to be efficient. Such skills include AI fundamentals, AI ethics, programming and coding, AI evaluation, etc. However, for employees to attain new skills and knowledge, the library must organise training programmes for the employees. Librarians’ jobs include cataloguing and classification, resource acquisition, and reference services. etc. Using AI to reduce the tasks requires skills and knowledge. As a result, staff training on AI in university libraries is necessary. Awogbami, Opele, and Adeoye (2021) affirmed that the library profession is dynamic and problematic, and the most productive and effective way for professional librarians to address these problems is to look for professional development programmes. Similarly, Ahanya, Vasudevan, and Prasanth (2021) stated that professional librarians require training to improve their job performance, expand their knowledge, and become experts in technological advancements.

7. Conclusion and Recommendations

8. Implications

The findings of this study have significant implications for library staff training on the use of technologies. Staff training in this study is tailored to enhance the use of AI for library operations. The university librarians should encourage all librarians in the university libraries to develop themselves to be relevant in this digital age. Furthermore, university library management should allocate funds toward staff training programs that will enable librarians to acquire the skills and knowledge to be experts in technologies, thereby simplifying the effective use of AI for library operations. Librarians in university libraries should make themselves available for the training.

Suggestions for Future Studies

• ⅰ. Only librarians in university libraries were examined. Consequently, this limits the chance of getting diverse answers on staff training and the use of AI in other academic libraries in Nigeria and other countries. Therefore, future studies on a similar topic should examine AI training needed by other types of libraries aside from university libraries in Nigeria and other countries.
• ⅱ. Future studies should explore how training on AI tools like text-to-speech or screen readers can help library personnel make library resources accessible for users with disabilities.
• ⅲ. Future studies should examine the long-term impact of AI training on library performance using longitudinal studies.
• ⅳ. Finally, future studies should research different approaches involved in AI training or assess the comparative effectiveness of AI training methods.

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