Exploring the Integration of Artificial Intelligence in Academic Libraries: A Study on Librarians’ Perspectives in India

2.1 Introduction to AI in Libraries

AI has emerged as a transformative technology across various sectors, including libraries. The application of AI in libraries represents a significant evolution from traditional methods of managing and disseminating information to more dynamic and interactive processes. Libraries have historically been early adopters of technology, striving to enhance their services and reach a broader audience. The integration of AI into library systems promises to further this mission by offering personalized user experiences, automating repetitive tasks, and enhancing the accessibility and organization of vast amounts of information (Cox, Pinfield, & Rutter, 2018; Gill et al., 2022; Okunlaya, Syed Abdullah, & Alias, 2022; Rubin, Chen, & Thorimbert, 2010).

AI technologies can analyse vast datasets at unprecedented speeds, identifying patterns and insights that would be impossible for humans to discern unaided (Bhuiyan, Ahmed, & Jahan, 2024; Lund & Wang, 2023; Senathirajah et al., 2024). This capability is particularly beneficial in libraries, where managing extensive collections of books, journals, multimedia, and digital content is a complex task. AI can streamline cataloguing processes, improve search functionalities, and even predict user needs based on past interactions. These advancements help create a more intuitive and efficient system that aligns with the evolving expectations of library patrons (Adewojo & Dunmade, 2024; Jayavadivel et al., 2024; Lu, 2024; Nardi & O’day, 1996).

Globally, libraries are experimenting with various AI applications to create smarter, more responsive systems. For instance, AI can facilitate more effective information retrieval through natural language processing (NLP), allowing users to search for information using conversational queries rather than keyword-based searches. This not only improves user satisfaction but also makes information more accessible to those who may not be familiar with specific terminology. Moreover, AI’s ability to learn and adapt over time means that library systems can continuously improve, providing ever-more accurate and relevant results to users (Ali, Naeem, & Bhatti, 2020; Collins, Dennehy, Conboy, & Mikalef, 2021; Noh, 2015; Taskin & Al, 2019).

2.2 AI Applications in Library Services

The application of AI in library services is vast and continually expanding, with numerous practical implementations enhancing both the user experience and operational efficiency. One prominent example is the use of chatbots. These AI-driven assistants provide 24/7 support to library users, answering common queries, guiding users through resource databases, and offering assistance with basic library services (Jha, 2023; Li & Coates, 2024; Panda & Chakravarty, 2022). Chatbots can handle a variety of tasks simultaneously, reducing the workload on library staff and ensuring that patrons receive timely and accurate information (Ehrenpreis & DeLooper, 2022; Hamad, Al-Fadel, & Fakhouri, 2023).

Another significant application is AI-powered recommendation systems. These systems analyse user data, including borrowing history and search patterns, to suggest books, articles, and other resources that match users’ interests. This personalized approach not only improves user engagement but also helps users discover new materials that they might not have found otherwise (Devika & Milton, 2024; Priya & Ramya, 2024). Automated cataloguing and metadata generation are also crucial AI applications in libraries (Brzustowicz, 2023; Greenberg, Spurgin, & Crystal, 2006; Hechler, Weihrauch, & Wu, 2023). Traditional cataloguing processes are labour-intensive and time-consuming (Chandrakar & Arora, 2010). AI can streamline these processes by automatically generating metadata for new acquisitions, ensuring that resources are correctly classified and easily retrievable. This automation not only saves time but also reduces the potential for human error, leading to more accurate and comprehensive library catalogues.

Digital preservation is another area where AI plays a pivotal role. Libraries around the world house vast collections of historical documents, manuscripts, and other materials that require careful preservation. AI technologies can assist in the digital restoration and preservation of these materials, using image recognition and processing algorithms to repair and enhance damaged texts and images. This ensures that valuable historical records are preserved for future generations.

In Asia, libraries are leveraging AI to overcome specific regional challenges, such as language barriers. AI-driven translation and language processing tools help libraries provide access to resources in multiple languages, making information more accessible to diverse user bases. This application is particularly significant in multilingual countries like India, where providing services in various local languages can be challenging.

2.3 Perceptions and Attitudes Towards AI in Libraries

The successful implementation of AI in libraries is heavily influenced by the perceptions and attitudes of library professionals. Understanding these perceptions is crucial for developing strategies that encourage the adoption of AI technologies. Research indicates a spectrum of attitudes among librarians, ranging from enthusiastic acceptance to cautious scepticism (Huang, 2022; Kashive, Powale, & Kashive, 2020; Mckie & Narayan, 2019).

Many librarians view AI as a powerful tool that can revolutionize library services. They recognize the potential for AI to automate routine tasks, freeing up time for more complex and creative activities. For example, AI can handle repetitive cataloguing duties, allowing librarians to focus on curating unique collections, conducting research, and engaging with the community. Additionally, AI’s ability to provide personalized recommendations and enhance search capabilities is seen as a significant improvement in user services (Hamad et al., 2023; Lund, Omame, Tijani, & Agbaji, 2020; Wang, 2017; Wood & Evans, 2018).

However, there is also apprehension about the integration of AI in libraries. One of the primary concerns is job displacement. Librarians worry that the automation of tasks traditionally performed by humans could lead to job losses. This fear is compounded by the rapid pace of technological advancement, which can make it challenging for professionals to keep up with new developments and acquire the necessary skills (Huang, 2022; Okunlaya et al., 2022; Shen & Zhang, 2024; Subaveerapandiyan & Gozali, 2024). Addressing these concerns requires transparent communication about the role of AI as a complementary tool rather than a replacement for human labour.

Ethical considerations also play a significant role in shaping attitudes towards AI. Librarians are often wary of the implications of AI on data privacy and security. The use of AI involves the collection and analysis of large amounts of data, raising concerns about how this data is used and protected. Ensuring that AI systems are designed and implemented with robust privacy safeguards is essential for gaining the trust of library professionals and users alike (Adigun, Ajani, & Enakrire, 2024; Lund & Wang, 2023; Panda & Chakravarty, 2022).

Research by Mishra and Sarvaiya (2024) and others emphasizes the importance of on-going education and ethical training for librarians. Providing opportunities for professional development and creating platforms for librarians to share their experiences and concerns can help mitigate resistance to AI. Furthermore, involving librarians in the development and implementation of AI systems can ensure that these technologies are tailored to meet the specific needs and values of the library community.

2.4 Challenges and Barriers to AI Integration

The integration of AI in libraries, while promising, is not without its challenges. Several barriers hinder the widespread adoption of AI technologies in library settings, including financial constraints, technical expertise gaps, and ethical concerns.

One of the most significant challenges is the financial investment required to implement AI technologies. Developing and maintaining AI systems can be expensive, and many libraries, particularly those in developing regions, struggle with limited budgets (Harisanty, Anna, Putri, Firdaus, & Noor Azizi, 2022; Subaveerapandiyan & Gozali, 2024). The cost of acquiring AI software, hardware, and the necessary infrastructure can be prohibitive. Additionally, on-going maintenance and updates are essential to keep AI systems functioning effectively, adding to the financial burden. Securing funding for AI initiatives often requires libraries to seek external grants, partnerships, and innovative funding models (Ali et al., 2020; Barsha & Munshi, 2023; Kaushal & Yadav, 2022).

Another critical barrier is the skills gap among library staff. Implementing and managing AI technologies requires specialized technical knowledge that many librarians currently lack. This skills gap necessitates substantial investment in training and professional development to equip library staff with the necessary competencies. Workshops, courses, and certifications focused on AI and related technologies can help bridge this gap, but these initiatives also require time and financial resources (Ayinde & Kirkwood, 2020; Cox et al., 2018; Diseiye, Ukubeyinje, Oladokun, & Kakwagh, 2024).

Ethical issues further complicate the integration of AI in libraries. Concerns about data privacy, algorithmic bias, and the transparency of AI decision-making processes are paramount. Libraries handle sensitive user data, and ensuring that AI systems comply with data protection regulations is crucial. Algorithmic bias is another concern, as AI systems trained on biased datasets can perpetuate and even exacerbate existing inequalities. Ensuring transparency in AI decision-making processes is essential to maintain user trust and uphold the ethical standards of library services (Hodonu-Wusu, 2024; Rajkumar et al., 2024).

Studies by Liang et al. (2023) and Ledro et al. (2022) provide insights into these challenges and suggest strategies for overcoming them. One approach is to develop clear ethical guidelines for the use of AI in libraries, addressing issues of data privacy, bias, and transparency. Collaborating with technology providers to design AI systems that align with these ethical standards is also crucial. Furthermore, fostering partnerships with academic institutions and other libraries can facilitate knowledge-sharing and collaborative problem-solving.

2.5 AI in Indian Libraries: Current State and Future Prospects

In India, the adoption of AI in libraries is still in its early stages, but there are promising developments that signal a bright future for AI-driven library services (Chatterjee & Bhattacharjee, 2020; Dwivedi et al., 2021). Indian libraries face unique challenges, including limited funding, infrastructural constraints, and the need for solutions tailored to diverse linguistic and cultural contexts (Subaveerapandiyan & Gozali, 2024).

The current state of AI in Indian libraries is characterized by experimental implementations and pilot projects. For instance, some academic libraries have begun using AI-powered recommendation systems to enhance the research experience for students and faculty. These systems analyse user behaviour and preferences to suggest relevant resources, making the research process more efficient and personalized. Additionally, AI-driven chatbots are being introduced to provide round-the-clock assistance to library users, helping them navigate databases and find information quickly (Kaushal & Yadav, 2022; Khan, Gupta, Sinhababu, & Chakravarty, 2023; Lappalainen & Narayanan, 2023).

Despite these advancements, the widespread adoption of AI in Indian libraries is hindered by several challenges. Limited funding is a significant barrier, as many libraries operate on tight budgets and cannot afford the high costs associated with AI technologies. This financial constraint necessitates innovative funding strategies, such as seeking grants from governmental and non-governmental organizations or forming partnerships with tech companies and academic institutions. Collaborative efforts can help pool resources and share the financial burden, making AI technologies more accessible to Indian libraries (Balasubramanian & Tamilselvan, 2023; Ram, 2024; Verma & Gupta, 2022).

2.6 Ethical and Professional Considerations

The ethical use of AI in libraries is a critical area of concern, requiring careful consideration of issues such as data privacy, transparency in AI decision-making, and ensuring fairness in AI algorithms. Librarians play a vital role in managing these ethical issues, necessitating adequate training and guidelines to navigate the complexities of AI technologies (Morley et al., 2023; Ng, Leung, Chu, & Qiao, 2021; Stahl, 2021).

Data privacy is one of the foremost ethical considerations when implementing AI in libraries. AI systems often rely on large datasets, which can include sensitive user information. Ensuring the confidentiality and security of these data is paramount (Ali et al., 2020; Gasparini & Kautonen, 2022). Libraries must adopt robust data protection measures, complying with legal and regulatory standards such as the General Data Protection Regulation in Europe (Banciu & Mantykangas, 2018; Marc, 2018). Transparency in AI decision-making processes is another crucial ethical consideration (Bradley, 2022; Lowagie, 2023). Libraries should prioritize the use of explainable AI (XAI) techniques, which aim to make AI decision-making processes more interpretable and understandable. Providing users with explanations for AI-generated recommendations and decisions can enhance transparency and accountability.

Professional development and ethical training for librarians are critical for managing AI technologies responsibly. Librarians need to be equipped with the knowledge and skills to understand the ethical implications of AI and make informed decisions about its use. Continuous education programs, workshops, and certifications focused on AI ethics can provide librarians with the necessary competencies (Ghosh & McCoy, 2024; Martzoukou, 2020; Nguyen, Ngo, Hong, Dang, & Nguyen, 2023). Additionally, creating platforms for librarians to share their experiences and discuss ethical challenges can foster a community of practice that promotes ethical AI usage.

3.1 Sampling Technique and Sample Size Determination

Purposive sampling was employed in this study to target specific individuals with relevant knowledge and experience in LIS within India. The selected population comprised college and university librarians across India, who are actively engaged in library services and likely to provide valuable insights into AI integration in academic libraries. This sampling strategy was chosen for its effectiveness in obtaining rich and detailed information from a specialized group.

A total of 300 emails were distributed, resulting in 259 responses, indicating a response rate of 86.3%. Data collection took place from 1 to 29 February 2024. This high response rate suggests a strong interest and engagement among the targeted respondents.

3.2 Survey Design and Questionnaire Development

A structured questionnaire was developed based on the study objectives and reviewed by subject matter experts in LIS and AI. The questionnaire comprised three sections: demographic information, perceived benefits of AI implementation in library services, and awareness of AI applications among LIS professionals. The questionnaire items were designed using a 5-point Likert scale to gauge respondents’ perceptions.

The development of the questionnaire followed a systematic approach, starting with the creation of an initial item pool derived from the study objectives. This item pool underwent a thorough review by experts in LIS and AI to ensure content validity and relevance. Subsequent revisions were made based on the feedback received, followed by pilot testing with a small sample of respondents to evaluate the clarity, comprehensibility, and appropriateness of the questionnaire items. Feedback from the pilot study was carefully considered, leading to further refinements to enhance the questionnaire’s validity and reliability.

To ensure a comprehensive understanding, the questionnaire was divided into the following sections:

• Demographic Information: Collected data on the respondents’ age, gender, educational qualifications, and years of experience in LIS.

• Perceived Benefits of AI: Assessed the perceived advantages of integrating AI in library services, such as improved efficiency, enhanced user experience, and data management capabilities.

• Awareness and Use of AI Applications: Evaluated the respondents’ familiarity with various AI applications and their current usage in library operations.

3.3 Data Collection

The survey was conducted using Google Forms, with the survey link distributed through personal email IDs of LIS professionals. The use of an online survey platform facilitated efficient data collection and management. Reminders were sent to non-respondents to maximize the response rate.

3.4 Data Analysis

Descriptive statistics such as frequencies, percentages, means, and standard deviations were calculated to analyse the demographic profile of respondents and their perceptions of AI in library services. Inferential statistics, including mean and standard deviation, were used to assess significant differences in respondents’ perceptions based on demographic variables. The statistical analysis was conducted using software packages such as SPSS 29.

5.1 Implications

The study’s findings hold significant implications for various aspects of academic libraries in India:

The insights gained can inform policy and practice within academic libraries, aiding policymakers and administrators in developing strategic plans for AI integration. Understanding librarians’ perspectives and attitudes towards AI can guide decision-making processes and resource allocation for effective implementation.

There’s a pressing need to incorporate AI education and training programs into LIS curricula and professional development initiatives. Equipping LIS professionals with AI-related skills and knowledge can enhance their readiness to adapt to evolving library environments.

Interdisciplinary collaborations between LIS professionals and AI researchers hold promise for developing innovative AI-driven solutions tailored to academic library needs. These partnerships can lead to enhanced service delivery and user experiences.

Addressing librarians’ concerns about AI’s impact on employment and resource allocation is crucial. Transparent communication, professional development opportunities, and proactive measures can help mitigate potential negative consequences.

Embracing AI fosters a culture of innovation within academic libraries, paving the way for the exploration and adoption of AI-powered services and technologies. This can revolutionize library services, improve efficiency, and enhance user experiences.

The study suggests future research directions, such as longitudinal studies to assess the long-term impact of AI integration and comparative studies across regions to understand variations in perspectives and practices. Qualitative research methods can delve deeper into librarians’ perceptions and experiences regarding AI in library services, enriching our understanding of the phenomenon.

6.1 Limitations

While efforts were made to conduct the study rigorously, certain limitations are inherent in the research design. Purposive sampling may limit the generalizability of the findings to the broader population of LIS professionals in India. Additionally, reliance on self-reported data through surveys may introduce response bias or social desirability bias. Furthermore, the cross-sectional nature of the study restricts the ability to infer causality or temporal relationships between variables.

6.2 Future Directions

Future research could explore longitudinal studies to track the long-term impact of AI integration in academic libraries. Comparative studies across different countries or regions could also be conducted to assess variations in perspectives and practices. Moreover, qualitative research methods such as interviews or focus groups could provide deeper insights into the lived experiences and perceptions of LIS professionals regarding AI in library services. Interdisciplinary collaborations between LIS and AI researchers could facilitate the development of innovative AI-driven solutions tailored to the specific needs and challenges of academic libraries in the digital age.