Sage Journals: Discover world-class research

Abstract

This essay explores the potential impact of Generative Artificial Intelligence (GAI) on academic library access services, highlighting both the benefits and challenges of incorporating GAI tools into these services. The integration of AI-driven tools like chatbots can revolutionize library access services, making them more efficient, user-centered, and adaptive to the needs of diverse user communities. GAI can enhance search and information retrieval, provide personalized learning and development, optimize circulation management, improve research support and accessibility, and enhance community engagement. However, challenges such as accuracy and reliability, data privacy, bias in AI algorithms, user acceptance and adoption, scalability and interoperability, technology dependence, occupational displacement, financial challenges, staff training and expertise, security threats, and change management must be addressed. To successfully implement GAI in academic library access services, librarians and managers must adopt a strategic and thoughtful approach, engaging in meticulous planning, collaboration, and strategic investment in infrastructure and expertise. By harnessing the power of GAI, academic library access services can drive innovation, improvement, and enhancement of the user experience, leading to heightened user satisfaction and improved operational efficiency.

Keywords

academic libraries Generative AI access services artificial intelligence GAI

Introduction

The recent advancements in artificial intelligence (AI), specifically in the realm of generative AI, have sparked excitement and curiosity across various industries, and academic libraries are no exception. Academic libraries have long been at the forefront of embracing technological innovations to enhance their access services and improve user experiences.^1–3 This essay explores the potential impact of generative AI on academic library access services and discusses the opportunities and challenges that lie ahead. The focus is on understanding how generative AI reshapes access services and the importance of successfully navigating its challenges to leverage its potential in a digital environment that is changing so rapidly.

Academic libraries serve as vital institutions for knowledge dissemination, information access, and research support in higher education. Traditionally, access services in academic libraries have revolved around managing collections, facilitating user access to materials, and providing guidance on information retrieval. However, the advent of Generative AI (GAI) technologies presents an opportunity to revolutionize academic library access services, making them more efficient, user-centered, and adaptive to the needs of diverse user communities.

GAI has the potential to transform access services in academic libraries by providing tailored recommendations, simplifying discovery and exploration, and automating metadata creation.⁴ This will allow staff to focus on tasks requiring human creativity and supports strategic decision-making with actionable insights.⁵ The integration of AI-powered systems in access services also comes with certain challenges including technical problems and ethical and legal issues, which require careful deliberation. Therefore, academic libraries need to adopt a strategic and considerate approach towards GAI integration in their access services to harness its advantages while also addressing ethical and societal concerns.^2,6

Definition of Generative AI

Generative AI (GAI) is a form of artificial intelligence that has the capability to produce unique content, including text, images, video, audio, or software code, based on a user’s instruction or request.⁷ Feuerriegel⁸ defines the term “generative” in relation to AI as the ability of an AI system to autonomously create new content without the need for human intervention. GAI operates using complex machine learning models known as deep learning models. These models mimic the human brain’s learning and decision-making processes. They function by recognizing and encoding patterns and relationships within vast quantities of data. This information is then utilized to comprehend users’ natural language prompts or queries and generate pertinent new content in response.⁹

GAI, for instance, can be used to create chatbots.¹⁰ A number of companies, including OpenAI, Google, DeepMind, and Meta, have developed such chatbots. ChatGPT, a generative AI, has been gaining popularity ever since OpenAI made its user interfaces publicly accessible in November 2022. According to Ali,¹¹ ChatGPT has a good future with academic libraries and information centers. The author sees it as the helping hand of the library to facilitate the information to users and information lovers.

Even though these technologies are still in the early stages, they have already started to draw the attention of academic researchers, and their reliability is also frequently assessed by experts.^6,12 While some argue that ChatGPT is the best AI chatbot available to the general public, critics raise issues with its potential adverse effects on the quaternary sector and the ethical implications of its usage.^6,12 These contrasting views underscore the complex discussion around ChatGPT’s role in both academia and society.²

Definition of access services

Academic libraries serve as vital resources in the educational ecosystem, supporting research, teaching, and learning by providing access to a rich array of information resources. Access services in academic libraries encompass various strategies and operations designed to facilitate equitable access to these resources, including user education, information desk, circulation, reference, course reserve (print and electronic), security, resource sharing such as interlibrary loan (ILL), entry/exit control, stacks maintenance, access to electronic resources, and user assistance.¹³

Access services act as a gateway to library resources, offering a single point of contact for users to access information, assistance, and support. Harper¹⁴ opined that access services is dedicated to creating and offering services that bridge the gap between library users and library resources. As the main touchpoint for library users, access services are committed to ensuring a positive and fulfilling experience for all users within the library.

Review of the literature

Generative Artificial Intelligence (GAI), also known as Generative AI, is a cutting-edge technology that is currently being utilized across various sectors to provide services.¹⁵ Despite being a relatively new area of study, a growing body of research has emerged in the field of library and information science, exploring the implications of GAI on library services, including its potential impact on circulation management,⁴ analyze and interpret the library data,¹¹ delivery of tailored content,^2,16 the integration of new technologies in libraries,^17,18 reference services,¹⁹ the implications of ChatGPT for academic libraries,^20,21 the development of similarity indices,²² its effects on academia and libraries,²³ and its applications in library management.²⁴

Meanwhile, Cox²⁵ posits that because libraries are starting to use artificial intelligence (AI), they need to produce new ways of working with information. This means that librarians need to be able to understand and work with data and algorithms. To deal with the problems of too much information and people not having access to it, the authors suggest creating an “Intelligent Library” that uses AI and other advanced technologies to make it easier for people to find the information they need.

Potential applications of generative AI in access services

In the context of academic libraries, Generative AI can revolutionize library access services in several ways. Librarians tend to adopt innovative technologies that expand their services, as they are catalysts for modern and innovative technology.^1,18 Using Natural Language Processing (NLP), Generative AI can analyze user queries and suggest relevant resources, including articles, books, and databases. This capability can streamline the research process for students and faculty, allowing them to locate needed materials more efficiently. Other major potential of integrating AI-powered systems in academic library access services is as follows.

Enhanced search and information retrieval

One of the primary functions of academic library access services is to provide users with efficient and effective information retrieval. GAI can enhance this process by enabling more intuitive and user-friendly search experiences.^2,26 Traditional search algorithms rely on keyword matching and relevance ranking, which may not always capture the user’s intent or context. GAI models, trained on vast amounts of textual data, can understand and interpret user queries more semantically, generating responses that go beyond simple keyword searches.¹⁶

For example, a study by Khan⁴ demonstrated how a generative AI model could improve information retrieval in academic libraries. The model, trained on a large corpus of scholarly articles, was able to understand complex user queries and provide relevant and contextually appropriate search results. The authors suggested generative AI could revolutionize the way users interact with library search systems, making information retrieval more efficient and effective. Asemi²⁷ opined that the implementation of expert systems in libraries can significantly augment library services by facilitating personalized recommendations, optimizing information retrieval processes, and expanding user access to relevant resources.²⁷

Personalized learning and development

GAI can have a profound impact in the creation and delivery of tailored content.^2,3,16 GAI can provide more personalized user experiences by acting as a virtual library assistant. Chatbots and AI-driven FAQs can address common queries, guide users to relevant resources, and offer 24/7 support, thereby extending access beyond traditional hours.^4,24 Academic library access services often strive to provide customized resources and services to meet the diverse needs of their user communities. GAI can analyze user behavior, preferences, and feedback to create personalized content, such as study guides, research tutorials, or even customized reading lists.⁶

Efficient circulation management

The integration of AI-powered systems can transform circulation management in academic libraries, offering a more efficient and streamlined experience for patrons.^4,24 For instance, AI-driven tools like ChatGPT can automate the check-in and check-out process, significantly reducing wait times at the service desk. Moreover, AI-powered metrics such as Perplexity can help evaluate the coherence and accuracy of AI-generated responses, ensuring that patrons receive reliable information during interactions.⁴

Enhanced research support and accessibility

Khan’s⁴ case study highlighted the effectiveness of a GAI chatbot in providing research support to library users. The chatbot, trained on a corpus of reference inquiries and research methodologies, was able to engage in natural language conversations with users, offering initial guidance and directing them to relevant resources or experts. The study demonstrated the potential of Generative AI to enhance the accessibility and efficiency of research support services. Chen^19,23 looked into a ChatGPT study and how it affected the reference services offered by libraries. Chatbots’ round-the-clock accessibility was one of their main benefits for libraries. It is available for users to use whenever they want, without waiting for library staff to be in the library. As a result, users get a quick and easy solution while efficiency is increased.

Improved accessibility and inclusivity

AI-driven accessibility tools are transforming how inclusive library resources are by introducing advanced features like text-to-speech, speech recognition, and language translation. These innovative tools enable people with visual impairments, language limitations, or other accessibility challenges to fully access and enjoy library content. Additionally, AI-enabled Natural Language Generation (NLG) technologies facilitate the production of alternative content formats, such as audio descriptions and simplified texts, making library materials accessible to a broader and more diverse audience.^2,28

Optimized interlibrary loan processes

Generative AI can optimize interlibrary loan processes by predicting user needs based on past borrowing patterns and improving communication and collaboration among libraries. By leveraging automation technologies in the lending process, access services in academic libraries can streamline repetitive and time-consuming tasks, enabling librarians, access services managers, and other staff to focus on more strategic initiatives.²

Improved collection management

AI-powered systems can help access services in academic libraries to improve collection management. For example, the implementation of GAI can allow academic library access services librarians and managers to analyze vast amounts of usage data to identify trends and user needs. This data-driven approach can inform collection development, identify gaps in the collection, resource allocation, and service improvement efforts, ultimately enhancing access services.^4,29

Enhanced community engagement

Implementing GAI in academic library access services can also enhance community engagement. AI-powered systems can help facilitate virtual events, workshops, and webinars, expanding the library’s reach and engagement with the community. Adopting AI-powered systems can also help access services librarians and managers track engagement metrics, such as attendance, participation, and feedback, to evaluate the effectiveness of community outreach efforts.

Security and fraud detection

By leveraging artificial intelligence, academic library access services can significantly bolster their cybersecurity defenses. AI-driven systems can pinpoint anomalies, scrutinize access logs, and flag potential security threats, ensuring the integrity of library resources and systems. Advanced machine learning algorithms can enable the detection of suspicious activities, unauthorized access attempts, and fraudulent behavior, allowing libraries to respond promptly to potential breaches. Furthermore, AI-powered fraud detection systems can identify instances of plagiarism, unauthorized content sharing, and intellectual property infringement, thereby safeguarding library collections and enforcing copyright policies.³⁰

Challenges and ethical considerations

Implementing Generative AI in academic library access services comes with challenges and considerations that must be acknowledged which are as follows.

Accuracy and reliability

One of the primary concerns is the quality and credibility of the generated content, especially in an academic setting where information integrity is of utmost importance.² To ensure the accuracy and reliability of AI-generated content, rigorous evaluation and validation processes must be implemented.

Academic library access services librarians and managers can play a pivotal role in developing standards and best practices for assessing the quality of AI-generated content. For example, the development of critical evaluation frameworks, similar to those proposed in references 31 and 32, can help librarians and users alike assess the credibility and ethical implications of AI-generated information. Educating users about information literacy in the age of AI is also essential, empowering them to critically evaluate and responsibly use AI-generated content.

Data privacy

The training of generative AI models often relies on large datasets, which may include sensitive user information. This raise concerns that artificial intelligence models could be used for malicious purposes, such as cybercrime or other illegal activities. Access services staff must carefully consider the privacy and ethical implications of using such data and implement robust data governance practices. This includes obtaining informed consent, ensuring anonymization techniques, and being transparent about data usage and protection.^23,33

Bias in AI algorithms

The biases present in training data can be learned and amplified by AI algorithms, resulting in discriminatory outcomes and unequal treatment of certain demographic groups. This means generative AI models can be unfair and discriminate against certain groups of people based on things like their skin color, gender, age, language, job, and where they live.³¹ This raises concerns about the fairness, accountability, and transparency of AI-generated content. Libraries must actively work to mitigate these biases, ensuring that their AI-powered access services are designed and implemented in a way that promotes equity, diversity, and inclusion.

User acceptance and adoption

Introducing AI-powered access services can be met with resistance from users and staff who are unfamiliar or uncomfortable with new technologies. This resistance can stem from a variety of factors, including preference for traditional services; difficulty adapting to new technologies; and reluctance to change among users and access services staff.^34,35 Academic library access services leaders must find ways to understand the root causes of resistance and develop strategies to address user and staff concerns including investing in user education and outreach to promote the benefits of AI-powered services.

Scalability and interoperability

AI-powered access services may need to be integrated with existing library systems, which can be a challenge, particularly if the systems are from different vendors or have different architectures. For example, integrating AI-powered systems with existing library systems, such as OPACs and ILSs, can be challenging, especially if these systems are outdated or lack APIs. Moreover, lack of standardization in data formats, APIs, and protocols can make it difficult for AI systems to integrate with other library systems.³¹

Technology dependence

AI-powered services may be unavailable or partially unavailable during system downtime, which can impact user experience and access to information resources. Additionally, academic library access services may rely on third-party services, such as cloud providers or AI platform vendors, which can be subject to outages, changes in terms of service, or other disruptions. An over-reliance on AI could diminish critical thinking skills among users and staff emphasizing the need for a balanced approach that combines human expertise with AI capabilities.³¹

Occupational displacement

The use of generative AI technology raises ethical questions, such as who owns the content that is generated and what the impact will be on employment.³⁶ The authors warned that this could lead to a few powerful companies controlling everything, causing people to lose their jobs, and limiting the freedom of workers and citizens. They added that the advent of generative AI could precipitate a discourse on occupational displacement, owing to its capacity for content generation and automation of repetitive tasks. Notably, generative AI’s proficiency in streamlining data-entry tasks has raised concerns about the potential obsolescence of jobs that rely heavily on this skillset.^3,37

In addition, a recent Pew survey shows that, about 37% of Americans are more worried than excited about AI becoming a bigger part of our daily lives. When asked why, one in five of these worried people said their main concern is that AI will take their jobs.³⁸ It could also lead to the spread of false information and have negative effects on the job market overall.

Financial challenges

Financial challenges are a critical barrier to implementing AI in access services in academic libraries. One of the main issues is the lack of adequate infrastructure in some institutions. GAI requires heavy and up-to-date technological tools to function successfully, and a lack of investment in infrastructure will hinder the successful implementation of AI in academic libraries.³⁹ According to Lo,⁴⁰ issues such as frequent updates and refinements to AI tools, the need for significant investment, and the potential for AI to be used in ways that do not benefit the library, or its users are all concerns some librarians have about AI-powered system implementation.⁴⁰

Staff training and expertise

The successful integration of generative AI requires a skilled access services staff capable of understanding and utilizing these technologies effectively. Academic library access services professionals need ongoing skill development opportunities to enhance their technical proficiency and adapt to the changing landscape. This includes training in areas such as machine learning fundamentals, data analytics, ethical AI, and user experience design, enabling them to provide effective support and guidance to library users.²⁵

Security threats

Large language models, such as ChatGPT, face security threats at every stage of their lifecycle. During training, bad actors might try to add fake or misleading information (poisoned or adversarial samples) to undermine performance or launch backdoor attacks to manipulate outcomes.⁴¹ Post-deployment, these models remain vulnerable to theft, with attackers employing smart model theft techniques to steal the model or its partial functioning. The evolving nature of security threats and the inherent complexities of generative AI models demand vigilant security measures and ongoing research to safeguard these systems and protect user data and privacy.³¹

Change management

Implementing AI-powered access services can require significant changes to library operations, policies, and procedures, which can be a challenge for libraries with traditional or bureaucratic cultures. Managing the transition from traditional library services to AI-powered services can impact workflows, staff roles, and user expectations. AI-powered services can automate certain tasks, which can lead to changes in workflows and job responsibilities, potentially impacting staff morale and productivity.³⁵ Library leaders have to communicate the benefits and impact of AI-powered services to staff and users and involve them in the decision-making process.

In summary, the incorporation of GAI tools in academic library access services presents numerous advantages, but the challenges outlined in this essay underscore the necessity for meticulous planning, collaboration, and strategic investment in infrastructure and expertise during the implementation process. It is essential for academic library access services librarians and managers to conduct a thorough assessment of the capabilities and limitations of GAI technologies, thereby ensuring that users receive an optimal experience that aligns with the library’s objectives and standards.

Concluding remarks

Generative AI presents a significant transformative potential for academic library access services. As GAI’s user base continues to grow, the responses to its technology have become more diverse, reflecting a wide range of opinions on innovation. While enthusiasts praise GAI chatbot like ChatGPT as a groundbreaking AI chatbot that is accessible to the general public, critics express concerns about its potential negative implications in the knowledge sector, democratic systems, and the moral dimensions of its deployment. This dichotomy of viewpoints underscores the complex discourse surrounding Gen-AI’s impact on academic library access services and society as a whole.

Access services librarians and managers must take a strategic and thoughtful approach to implementing GAI technology. Effective collaboration between librarians, access services managers, and designers is crucial in establishing clear expectations, mitigating concerns, and ensuring a smooth integration of GAI technology in academic library access services. Moreover, long-term strategic planning by library administrators is essential for making informed decisions and predicting future needs. By adopting GAI, access services units in academic libraries can drive innovation, improvement, and enhancement of the user experience. Ultimately, the integration of GAI has the potential to transform access services in academic libraries, leading to heightened user satisfaction and improved operational efficiency.

Statements and declarations

Footnotes

Conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Frank Boateng

References

Hussain

. Use of artificial intelligence in the library services: prospects and challenges. Libr Hi Tech News 2023; 40(2): 15–17.

Mallikarjuna

. An analysis of integrating artificial intelligence in academic libraries. J Lib Inf Technol 2024; 44(2): 124–129.

Enis

. Next gen AI: libraries work with chatGPT and other emerging AI tools. Libr J. https://www.libraryjournal.com/story/next-gen-ai-libraries-work-with-chatgpt-and-other-emerging-tools (accessed 14 September 2024).

Khan

Gupta

Sinhababu

, et al. Impact of conversational and generative AI systems on libraries: A use case Large Language Model (LLM). Sci Technol Libr 2023; 43: 1–15.

Adigun

Igboechesi

. Exploring the role of generative artificial intelligence in enhancing information retrieval and knowledge discovery in academic libraries. Int J Lib Inf Sci Stud 2024; 10(2): 1–14.

Gupta

Nair

Mishra

, et al. Adoption and impacts of generative artificial intelligence: theoretical underpinnings and research agenda. Int J Inf Manag Data Insights 2024; 4(1): 100232.

Pattam

. Transforming the data terrain through generative AI and synthetic data. Capgemini 2023. https://www.capgemini.com/insights/expert-perspectives/transforming-the-data-terrain-through-generative-ai-and-synthetic-data/ (accessed 19 August 2024).

Feuerriegel

Hartmann

Janiesch

, et al. Generative AI. Bus Inf Sy Eng 2023; 66: 111–126.

Stryker

Scapicchio

. What is generative AI? IBM Think. https://www.ibm.com/topics/generative-ai (2024, accessed 18 September 2024).

10.

Bernard

. A short history of ChatGPT: how we got to where we are today. https://www.forbes.com/sites/bernardmarr/2023/05/19/a-short-history-of-chatgpt-how-we-got-to-where-we-are-today/?sh=4293d56d674f/ (accessed 18 August 2024).

11.

Ali

. AI ChatGPT applications in libraries - challenges and opportunities. Bilgi ve Belge Araştırmaları Dergisi 2023; 20: 18–26.

12.

Watters

Lemanski

. Universal Skepticism of ChatGPT: a review of early literature on chat generative pre-trained transformer. Frontiers in Big Data 2023; 6: 1224976. https://doi.org/10.3389/fdata.2023.1224976

13.

Krasulski

Dawes

(eds) Twenty-first-century access services: on the front line of academic librarianship. 2nd ed. Chicago, IL: Association of College & Research Libraries, 2023.

14.

Harper

Forest

Kemi

, et al. A framework for access services librarianship: An initiative sponsored by the Association of College and Research Libraries. Access Services Interest Group, 2020. https://www.ala.org/sites/default/files/acrl/content/standards/acrl_access_services_framework.pdf (accessed 22 August 2024).

15.

Panda

Kaur

. Exploring the viability of ChatGPT as an alternative to traditional chatbot systems in library and information centers. Libr Hi Tech News 2023; 40(3): 22–25.

16.

Narayanan

. The era of generative AI: Transforming academic libraries, education, and research. Agnes College 2024: 282–293. https://zuscholars.zu.ac.ae/works/6351/ (accessed 16 September 2024).

17.

Fernandez

. Through the looking glass: envisioning new library technologies AI-text generators as explained by ChatGPT. Libr Hi Tech News 2023; 40(3): 11–14.

18.

Kautonen

Gasparini

. B-Wheel – Building AI competences in academic libraries. J Acad Librarian 2024; 50(4): 102886.

19.

Chen

. ChatGPT and its possible impact on library reference services. Internet Ref Serv Q 2023; 27: 1–9.

20.

Sanji

Behzadi

Gomroki

. Chatbot: an intelligent tool for libraries. Libr Hi Tech News 2022; 39(3): 17–20.

21.

Cox

Tzoc

. ChatGPT: implications for academic libraries. Coll Res Libr News 2023; 84(3): 99.

22.

Deep

kumar

. OpenAI ChatGPT generated content and similarity index: A study of selected terms from the library & information science. Ann Libr Inf Stud 2023; 70(2): 99–101.

23.

Lund

Wang

. Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Libr Hi Tech News 2023; 40(3): 26–29.

24.

Verma

. Novel study on AI-based chatbot (ChatGPT) impacts on the traditional library management. Int J Trend in Sci Res Develop 2023; 7(1): 961–964. https://www.ijtsrd.com/papers/ijtsrd52767.pdf (accessed 19 August 2024).

25.

Cox

Pinfield

Rutter

. The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Libr. Hi Tech 2019; 37(3): 418–435.

26.

Adetayo

. Artificial intelligence chatbots in academic libraries: the rise of ChatGPT. Libr Hi Tech News 2023; 40(3): 18–21.

27.

Asemi

Nowkarizi

. Intelligent libraries: a review on expert systems, artificial intelligence, and robot. Libr Hi Tech 2021; 39(2): 412–434.

28.

Emmanuel

Unwana

. The roles of artificial intelligence in library automation: the prospects and challenges. Erudite Compendiums in Educ 2024: 13–23.

29.

Whang

Roh

Song

, et al. Data collection and quality challenges in deep learning: a data-centric AI perspective. The VLDB J 2023; 32: 791–813.

30.

Kaur

Gupta

. Towards dissemination, detection and combating misinformation on social media: a literature review. J Bus Ind Market 2023; 38(8): 1656–1674.

31.

Wang

Pan

Yan

, et al. A survey on ChatGPT: AI–generated contents, challenges, and solutions. IEEE Open J Comput Soc 2023; 4: 280–302.

32.

Shal

Ghamrawi

Naccache

. Leadership styles and AI acceptance in academic libraries in higher education. J Acad Librarian 2024; 50(2): 102849.

33.

Andrieux

Johnson

Sarabadani

, et al. Ethical considerations of generative AI-enabled human resource management. Organ Dyn 2024; 53(1): 101032.

34.

Ogwo

Ibegbulem

Nwachukwu

. Applications and perceived impact of artificial intelligence in academic libraries in Nigeria. Libr Philos Pract 2023; 7907. https://digitalcommons.unl.edu/libphilprac/7907 (accessed 19 September 2024).

35.

Anderson

. AI in change management: early findings. Prosci. https://www.prosci.com/blog/ai-in-change-management-early-findings (2024, accessed 30 September 2024).

36.

Farina

Lavazza

. Ethical considerations and policy interventions concerning the impact of generative AI tools in the economy and in society. AI Ethics, 2024. AI Ethics 2025; 5: 737–745.

37.

Arntz

Gregory

Zierahn

. The risk of automation for jobs in OECD countries: A comparative analysis. In: OECD social, employment and migration working papers, No. 189. Paris: OECD Publishing, 2016. DOI: 10.1787/5jlz9h56dvq7-en.

38.

Rainie

Funk

Anderson

, et al. AI and human enhancement: Americans’ openness is tempered by a range of concerns. Pew Research Center, Technical Report. https://www.pewresearch.org/science/2022/03/17/ai-and-human-enhancement-americans-openness-is-tempered-by-a-range-of-concerns/ (2022, accessed 4 November 2024).

39.

Jha

. Application of artificial intelligence in libraries and information centers services: prospects and challenges. Libr Hi Tech News 2023; 40(7): 1–6.

40.

. Evaluating AI literacy in academic libraries: A survey study with a focus on U.S. Employees. https://digitalrepository.unm.edu/ulls_fsp/203 (2024, accessed 4 November 2024).

41.

Tian

Cui

Liang

, et al. A comprehensive survey on poisoning attacks and countermeasures in machine learning. ACM Comput Surv 2022; 55(8): 1–35.

The transformative potential of Generative AI in academic library access services: Opportunities and challenges

Abstract

Keywords

Introduction

Definition of Generative AI

Definition of access services

Review of the literature

Potential applications of generative AI in access services

Enhanced search and information retrieval

Personalized learning and development

Efficient circulation management

Enhanced research support and accessibility

Improved accessibility and inclusivity

Optimized interlibrary loan processes

Improved collection management

Enhanced community engagement

Security and fraud detection

Challenges and ethical considerations

Accuracy and reliability

Data privacy

Bias in AI algorithms

User acceptance and adoption

Scalability and interoperability

Technology dependence

Occupational displacement

Financial challenges

Staff training and expertise

Security threats

Change management

Concluding remarks

Statements and declarations

Footnotes

Conflicting interests

Funding

ORCID iD

References