Artificial intelligence tools and perspectives of university librarians: An overview

Abstract

The main purpose of this paper is to assess and examine the possible application of Artificial Intelligence (AI) tools in Pakistani academic libraries, particularly those areas of library technical and library user services where AI could be applied in the near future. A secondary purpose is to bring the library perspective on AI to the forefront of the scholarly world. This is a self-exploratory study, in which a qualitative approach interview has been conducted with 10 chief librarians/library heads (5 public + 5 private sectors) from universities regarding their views on the adoption of artificial intelligence tools in Pakistani academic libraries. Results are tabulated in a descriptive format. Librarians are aware of AI technologies. Services based on Natural Language Processing (NLP) are used in libraries, e.g. Google Assistant, Voice Searching, and Google Translate. Pattern recognition methods, such as text data mining, are also used to retrieve library material and conduct online searching. Big data is accessed via services such as cloud computing, OneDrive, and Google Drive. There is a very low level of awareness of robotics and chatbots. This study provides librarians with suggestions as to how AI tools could be used in libraries which either have yet to adopt AI technologies or wish to implement more advanced tools. Pakistani library schools could collaborate with computer science departments to establish AI Labs in the respective library and information science (LIS) departments/libraries. AI challenges funding and technological skills are the key problem to implement with AI in the University Libraries.

Keywords

Artificial intelligence big data chatbot machine learning natural language processing robotics text data mining

Introduction

Artificial Intelligence (AI) is one of the emerging trends in the world. Artificial Intelligence impacts on a wide range of disciplines, including medicine, surgery, automotive, aviation, business, industry, education, and all allied fields. Technology developments are enhancing library technical services and user services (Ali and Haider, 2016; Arlitsch and Newell, 2017). The effective use of Information and Communication Technology (ICT) tools are helping not only to modernize library services but also to make them unique within the institution.

Artificial Intelligence is also entering libraries through robotics, chatbot, Natural Language Processing, Big Data, and Text Data Mining. Artificial Intelligence impacts both library technical services and library user services. In technical services, it supports the management of library collection metadata, users’ data, and the usage of resources, through the application of tools such as Big Data and Text Data Mining. Library user services and information retrieval have seen the gradual introduction of tool such as chatbot, robotics, pattern recognition and natural language processing.

Initially, the term ‘AI’ was used in 1955 by J McCarthy when preparing a research proposal for the Dartmouth Summer Research Conference (McCarthy et al., 1955). Since then, there has been no universally accepted definition of artificial intelligence. It can be loosely interpreted as the incorporation of human intelligence into machines: ‘AI is a cluster of technology and approaches to computing focused on the ability of computers to make flexible rational decisions in response to unpredictable environmental conditions’ (Tredinnick, 2017, p. 37).

While there are many definitions depending upon the specific context, the researcher has chosen the following as most relevant to the current study: ‘AI refers to a field of computer science dedicated to the creation of systems performing tasks that usually require human intelligence’ (Jakhar, and Kaur, 2020, p. 1). According to Berendt (2019), the immediate output of AI is knowledge, which may be useful and understood by either a person or machine. In addition, AI has a social impact, including large social networks, search engines, and national and international surveillance programs (Berendt, 2019). It is considered as a social good for the transportation of knowledge and information.

Basically, artificial intelligence is comprised of two components known as Machine Learning and its subset, Deep Learning. Table 1 provides examples of AI tools which fall within these two major categories.

Table 1.

Artificial intelligence components.

Machine Learning	Deep Learning
Robotics	Image Processing
Chatbots	Natural Language Processing
Text Data Mining (TDM)	Pattern Recognition
Big Data

Machine learning

Machine Learning (ML) is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machines are playing a vital role in the library collection of resources and user services. Robotics, Chatbot, Text Data Mining (TDM), Big Data and Pattern Recognition are examples of Machine Learning tools within AI.

Deep learning

Deep Learning (DL) is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. Natural Language Processing (NLP) Image Processing (IM), Neural Networking are examples of AI tools used in the context of Deep Learning. In short, both Machine Learning and Deep Learning are used in libraries.

Literature review

Over 30 years ago, Hilker (1986) compiled an extensive list of the then current information resources for artificial intelligence. He could envisage the potential for AI to radically transform services areas, such as medical diagnostics (p. 14). Although not yet considered as a discipline, AI was deemed to offer new opportunities for expanding research. The references that follow help to corroborate the validity of Hilker’s vision.

AI is a broad, complex area of study, in which the purpose is for machines to work as human beings, behave as humans, and make human-like decisions. Computer and ICT devices are gradually being developed and designed to think and work as human beings, with the concept of Human Intelligence (HI) being converted into Artificial Intelligence (AI) (Asemi and Asemi, 2018).

Higher education is expected to be challenged by AI, robotics, and automation on multiple fronts. First and foremost, AI, robotics, and automation are replacing and will continue to replace jobs and revolutionize every nation’s economy and disrupt economic development in the world (Ma and Siau, 2018; Siau, 2017). Tredinnick (2017) has suggested that disruption in jobs will not come so much from replacing those jobs but rather in possibly undermining traditional role identity.

In a recent survey (Wood and Evans, 2018) of the perceptions of US academic librarians regarding the impact of AI, 56.3% responded that AI would have a transformative effect on librarianship and library services and yet only 47.42% expressed interest in attending a workshop in their library on AI. Wheatley and Hervieux (2019) have reported that while well-known libraries such as Stanford and MIT are actively engaging with AI technologies, AI has yet to become mainstream within academic libraries.

Wu et al. (2015) have focused on AI usage in the digital library search engine, CiteseerX, particularly in the context of citation analysis and the implementation of algorithm searching. The CiteseerX team has developed an AI approach to functions such as extracting metadata, deduplicating documents, and indexing tables for searching purposes.

Asemi and Asemi (2018) have examined the use of intelligent library systems in Iran in three areas: public/user services, technical services, and management services. They have used four criteria (Expert system/knowledge system, Intelligent decision support or recommender system, Data mining, and Natural language processing) to assess the extent to which AI has been applied. The authors have concluded that while Recommender Systems are the most developed in Iranian libraries, NLP is the least developed.

A survey based study from the experts, results identify that AI will developed in 2030, and gradually implemented in all the field of knowledge domain and profession (Müller and Bostrom, 2016)

A recent study conducted by Mahanty and Mahanti (2019) has examined the applications of AI to big data across a wide range of sectors. The authors have focused particularly on the types of insights which can be derived from the automation of complex, analytical tasks.

Robotics

From a social perspective, robotics is often viewed as the tangible embodiment of AI, because, unlike most traditional AI systems, robots are physical and interact with their environment (Ziemke, 2016). Robotics is one of the key AI tools which has been used in library services. As early as 2004, Prats et al. described the development of a librarian robot by Jaume I University to help library users find a book and retrieve it from the shelf. The main components of this system included user-robot interaction, sensor-based navigation, and book recognition. Later examples include automated retrieval centers, such as the one at the University of Utah’s J. Willard Marriott Library, in which machines are used to retrieve and shelve materials (Wang, 2017).

Phillips (2017) has conducted a comprehensive study on the usage of AI robotics in library services. He concludes:

The findings of the research, supported by the literature, indicate a general consensus that automation is perceived as positive where it releases humans from doing mundane or undesirable work. However, there are also genuine concerns that job losses may occur, and that there will not be enough new jobs to replace them. (p. 2)

A recent pilot study has discussed robotic library services in China (Wang, 2017). According to the author, robotics is regarded by some staff as very fast and accurate when used for searching for library materials, e.g. books. Robotics can be used to replace staff for tasks such as shelving and stock taking. At the same time, some staff expressed a feeling of insecurity when considering the potential future impact of robotics on their job.

Chatbot

Chatbots (also known as digital assistants, virtual agents or intelligent agents) are computer programs that can simulate an intelligent conversation, through text, speech or potentially through an embodied representation. They have been designed to interact with people by closely emulating human conversation, as idealized in the Turing Test for artificial intelligence (Bohle, 2018). Current examples of the use of chatbots in everyday life include Google Assistant and Amazon’s Alexa.

In relation to libraries, chatbots have been developed to answer directional and other predictable enquiries. They have the potential to be very effective in reference queries and reference services for library users (Mckie and Narayan, 2019; Meincke, 2018). Xiaotu is an example of one of the better known chatbots among librarians (Chen, 2018; Wang, 2017; Yao et al., 2015). Created as a smart talking robot for use in Tsinghua University Library, it has been described as presenting a ‘participatory library service, in which users participate in the resources collection and become content co-creators’ (Yao et al., 2015, p. 245). Its functions include Chinese natural language processing and self-learning. The researchers reported that the library staff interviewed were positive about the adoption of AI technologies into reference services; the former concluded that ‘Librarians do not have to worry that Xiaotu may possibly replace them’ (Yao et al., 2015, p. 255).

Natural Language Processing (NLP)

Natural Language Processing (NLP) emerged in the 1970s. Subfields of computer science and linguistics are still dedicated to nuances of NLP (Edgcomb and Zima, 2019), including its vital role in electronic health management systems. Natural Language Processing in search engines, e.g. Google and YouTube, is an example of AI already used commonly by information users. In addition to English, other languages have progressed from tolerating typographical errors when searching to offering the ability to conduct searches via voice. According to Wolfram (2016), NLP is helpful in designing subject indexing, bibliometrics, and information retrieval systems as key components in the creation of a digital library.

Pattern recognition

Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases. The use of CAPTCHA (Completely Automated Public Turing test), for example, to verify whether or not a user is human is widespread.

Different codes are connected with library data barcodes, e.g. QR code; similarly, different digital gadgets are also used for access to, and the security of, information. Library users can check in/out library materials with a single sweep. As mentioned by Ali (2017), libraries in Pakistan are replacing humans, e.g. security guards, with RFID and electronic security gates to protect materials from theft and vandalism.

Big data

The term ‘big data’ refers to data that is so large, fast or complex that it is difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time. According to Aradau and Blanke (2015), big data can be considered as artificial intelligence in an environment characterized by new relations between human and computers; this has led to major security concerns.

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It is what organizations do with the data that matters. For example, big data can be analyzed for insights that lead to better decisions and strategic business moves. Tuarob et al. (2016) have proposed the use of Algorithm Seer, a prototype search engine, to extract algorithms from scholarly Big Data.

Text Data Mining (TDM)

Text mining technology is now broadly applied to a wide variety of government, research, and business needs. All three groups may use text mining for records management and searching documents relevant to their daily activities. Legal professionals may use text mining for e-discovery. Governments and military groups use text mining for national security and intelligence purposes. Scientific researchers incorporate text mining approaches into efforts to organize large sets of text data (i.e., addressing the problem of unstructured data), to determine ideas communicated through text (e.g., sentiment analysis in social media) (Paltoglou and Thelwall, 2012; Pang and Lee, 2008). Given the significant ways in which the methods for acquiring and reusing data are changing, scholars will need to come to new agreements on what constitutes reliable and valid descriptions of the data; the categories used to organize those data; and the tools necessary to access, process, and structure these data (Shah et al., 2015).

Table 2.

Artificial intelligence tools used in library technical and user services.

Artificial Intelligence Tool	Technical Services			User Services
Chatbot	Acquisition	Descriptive Cataloguing	–	Query Services	Library Instructions	Information Retrieval
Robotics	Library Stock Taking	Shelving	–	Searching Library Material	Check In/Check Out	–
Natural Language Processing (NLP)	Knowledge Management	Information/Book Processing	Classification of Books	Translation of Text from Native Language	Reading of Material	Information Retrieval
Big Data	Library Resource Usage	Managing Repository	Library Data Storage/Ware house	Managing Repository	Library Usage Report	–
Text Data Mining (TDM)	Altmetric, Citations Support & Analysis	OPAC Searching	Metadata	Reference Services	#Library Trends	Social media Appearance
Pattern Recognition	Library Security Material	QR Code to the Material	Indexing and Abstracting of Image	–	Security Password/RFID	User identifications
Image Processing	Preservation and Archival	Managing Image and video library database	Medical Images/Scans Records	Library users Facial recognition	3D-Printings	–

Within technical services, AI applications can be found in functions such as collection development, subject indexing, and descriptive cataloging; examples within user services include reference services, database searching, and document delivery. Table 2 shows an overview of the current use of AI in both technical services and user services.

AI in Pakistani academic libraries

Pakistan university libraries are developing and implementing new approaches to modernizing their processes and services. Current status shows that approximately 80% university libraries in Karachi are automated; RFID technologies are also used in some libraries as a deterrent to theft and vandalism (Ali, 2017). The construction of the Digital Library (DL), e-content resources development, and the establishment of an institutional repository are in progress. Digital literacy, data stewardship, and data literacy are gradually being introduced. Similarly, not only are machines and systems being upgraded, but also current ways of thinking are gradually changing to incorporate a much greater use of technology in these libraries alongside manual work.

Khan and Bhatti (2018) have conducted a survey of the perceptions of university librarians and academicians in Pakistan about the usefulness of the Semantic Web for digital libraries. Because the Semantic Web enhances the ability of computers to search databases by using artificial intelligence and fuzzy logic, respondents were asked specifically about artificial intelligence software agents. The authors have reported that respondents ‘envisaged that, in future, the use of artificial intelligent software in a digital library shall [sic] provide more accuracy in results’ (p. 836).

Although not specifically focused on AI, a recent survey by Ahmad et al. (2019) has examined a related area: the implementation of big data analytics in academic libraries within Pakistan. The authors reported a strong correlation in the required competencies and skills of librarians toward the implementation of big data analytics in academic libraries. They concluded that there was a need to conduct relevant training to improve practices in such libraries.

To date no study has been conducted on librarians’ overall perceptions regarding AI in Pakistani academic libraries. This study is intended to fill that gap in the literature.

Research questions

To carry out this research, the researchers designed following research questions:

Q no. 1 How do you think AI tools could be used in Pakistani university libraries at the current time?

Q no. 2 How long do you think it will take until AI tools are widely integrated into university/academic libraries’ daily operations?

Q no. 3 Which AI tool do you think will soon be adopted widely in Pakistani academic libraries?

Q no. 4 Do you think that librarians should be concerned that AI tools may eventually replace their jobs?

Q no. 5 What are the Challenges (i.e., funding, Technological) to Implement AI tools in the University libraries of Pakistan?

Methodology

This is a qualitative exploratory research study. In this study, the authors have investigated two potential areas in which academic libraries in Karachi, Pakistan could apply AI: technical and user services. An open-ended questionnaire has been designed for recording the results of interviews with librarians as similar methods apply (Cox et al., 2018). This paper is an extract from a pilot study, which is being conducted among 10 top university library leaders/chief librarians selected for this interview, with 5 from public sector universities and 5 from private sector universities.

Limitation of the study

This paper covers the AI (subset and tools) in the context of academic library applications. Programming concepts such as computational algorithms, neural networks, and fuzzy logic are not part of this study. In addition, the study is limited to a specific region within Pakistan, i.e. Karachi, and a specific type of library, i.e. academic.

Results

Current application of AI in libraries

The open-ended responses to Q1 have been summarized in Table 3 below. Table 4 shows a low level of awareness of AI with no libraries using either robotics or chatbot. However, three libraries use pattern recognition, i.e. thumb verification for check in/check out. NLP has an average level of awareness of the usage of applications such as Google Assistant, Voice Searching and Google Translation. Big data usage has been linked to cloud computing, Google Drive, and OneDrive. Text data mining is also used in the retrieval of online information.

Table 3.

Current AI application in libraries.

Respondent	Q1 (Current Application of AI)
1	Pattern Recognition i.e. Thumb Verification, RFID 3M Gates
2	Pattern Recognition, Google Assistant
3	Data Mining, Catalogue Search Voice Search, Chat Reference Via Google Chat
4	Big Data in shape of Google Drive, Drive one, Cloud Computing
5	Natural Language Processing (NLP) Google Translation is used
6	Google Assistant, YouTube Voice Search, Google Voice Search, NLP Google Translation
7	Big data in shape of Cloud Computing, Drive one, Google Drive HEC digital and Data Text mining for the Searching of library material from different resources
8	AI is the starting point in libraries now. Voice searching and Google Translation in shape of NLP are used to start in libraries
9	Pattern recognition in Shape QR code, Bar Code and RFID Scanner for check in Check out and searching and security of Material
10	Data Text mining #HashTag are used in libraries, Voice Searching on Google

Table 4.

AI technologies in libraries.

Response	Pattern Recognition i.e. Thumb Verification	RFID, 3M Gates	Google Assistants	Google Translation	Voice Search	Data Mining	Big Data
1	✓	✓	X	X	X	X	X
2	✓	X	✓	X	x	X	X
3	X	X	✓	X	X	X	X
4	X	X	X	✓	X	X	✓
5	X	X	X	✓	X	X	X
6	X	X	✓	✓	✓	X	X
7	X	X	X	X	X	✓	✓
8	X	X	X	✓	✓	X	X
9	✓	✓	X	X	X	X	X
10	X	X	X	X	✓	✓	X
N = 10	3	2	3	4	3	2	2

AI effects on library services

Of the 10 responses received, 6 respondents (60%) believe that both technical and user services will be affected by AI. Whereas two respondents (20%) believe that only technical services will be affected, one respondent (10%) believes that only user services will be affected. One respondent (10%) has no idea which library services – if any – will be affected.

Integration of AI tools in university libraries

Five respondents (50%) believe that AI will be integrated into library daily operations within five to seven years. Three (20%) chose a time span of 5–10 years. One respondent (10%) believes that AI will be widely applied within 7–10 years, and one respondent (10%) believes that it will take about two decades to be widely applied in university libraries within Pakistan.

Job fear as a result of AI application in libraries

Technology is itself a source of fear within the library profession. Librarian were asked whether they think that AI will affect their jobs. Five (50%) have replied that they do not have any fear. Two (20%) think that their library designation or cadre will change. Two (20%) have responded that lower rank jobs, e.g. shelver and library attendant, will cease to exist. In addition, classification and cataloguing jobs job may also be candidates for removal in the library of the future. One (10%) has responded that ‘Data Analytics [sic] and Data Scientist will replace their job’.

Challenges implement to AI tools in the University libraries

Regarding the challenges and implementation of AI tools in the university libraries responded answer that (70%) response that fund and financial issue is the biggest challenges in the libraries. However, (30%) response that technological and information and communication system is the key challenges to AI applications in the University libraries. Responses are shown in Table 6.

Table 5.

Job fear of the librarian.

Response	RQ 4 (Job Fear)
1	No Job Fear, Librarian role will be change as information Officer
2	Classifier and Cataloguer Job will feel the effect in near future
3	Librarian designation will replace
4	Library Profession in Threat and library profession will vanish i.e. Steno Typist, Data scientist and Data Analytics will replace their job
5	No Job Fear
6	Librarian cadre will be change
7	Librarian job has no threat however, Lower post job will vanish
8	No Job Fear for the Librarian however, lower post shelvers and Library attendant post will no more due RFID and robotics
9	Library staff will reduce
10	No Job fear in current but in future It will be change library cadre

Table 6.

AI implementation challenges in the libraries funds or technological skills.

Response	Funds	Technologies
1	X	✓
2	✓	X
3	✓	X
4	✓	X
5	✓	X
6	X	✓
7	✓	X
8	✓	X
9	X	✓
10	✓	X
N = 10	7	3

Discussion

AI application in libraries

In Pakistani libraries, AI tools have not been fully implemented; however, interview responses from librarians have disclosed that some AI components are in use. Results show that Google Translation, Voice Search and Google Assistant are used for searching information. Natural Language Processing is being used in universities in Karachi. Pattern recognition, which is a key component of the Machine Learning field of AI, is also used by librarians. Examples include thumb impression and mobile pattern.

Social media, e.g. Twitter, is used in Pakistani academic institutions (Shahzad and Bilal, 2019) by both academics and librarians. In the context of Text data mining, the #tag (hashtag) is used on Twitter to find specific discussion topics. Access to big data in Pakistan has been facilitated at the national level through services such as the HEC National Digital Library, Pakistan Research Repository (PRR), and the high-speed Pakistan Education and Research Network (PERN).

The researchers found no university library response about robotics and chatbot applications or their potential future usage in libraries. They have, therefore, postulated two possible reasons. First, with the increase in information technology devices and systems in libraries, technical support for them may be an issue. Not all libraries can afford to hire a support person. Additionally, access to institutional information technology staff may not be available in all cases. Second, and as a corollary, user education would be required to use these technologies in the libraries.

Teaching, Learning and research in near future knowledge and information transformation float in new AI Technology (Dwivedi et al., 2019; Popenici and Kerr, 2017). Library system need more advance application for its users.

AI effect on the library

In their survey responses, academic library leaders are hopeful that AI will have a positive impact on both library technical and user services. For example, Semantic Web and ontology mapping may replace current indexing services. Search strategies will be developed in natural language, with the results retrieved via Voice Searching/Google Assistant. Chatbots (IBM Watson, Siri, Alexa by Amazon) may not only affect future library reference services but also reduce physical traffic to the library. Librarians – and users – will benefit from the development of more advanced scientometrics, e.g. the use of machine learning to provide more nuanced altmetrics. In the future, library systems based on AI may be designed to address more complex user needs as well as the functional needs of the library. AI adoption survey in libraries (Wood and Evans, 2018) only 47.42% they are interest in implanted to the libraries. Future arise that existing development in AI in near future intelligent library system will designed as per the need of the academic libraries (Asemi and Asemi, 2018; Cox et al., 2018).

Time span for AI application integration within libraries

In general, respondents felt that artificial intelligence would be fully integrated in university libraries within the next 5 to 7/10 years. Only one respondent felt that it would take much longer, i.e. 20 years. Results is similarly with other study describe that AI will reach in different walk of life 2030 to 2040 (Müller and Bostrom, 2016). In recommendation of another study (Wood and Evans, 2018) AI impact will feel within 10 years as our most of librarian suggested.

Respondents noted the important role of library schools in facilitating this. For example, they observed that it would be useful to establish AI laboratories in library schools to create an awareness about AI. An additional point raised by one respondent is the urgent need to update the curriculum of Pakistani library schools to improve the skillset of future librarians, e.g. including topics such as robotics, chatbots, NLP, and TDM. In this regard, a practical approach could be for LIS departments to consider integrating with computer science departments. As per the current integration of AI with the academic libraries within the organization as strategic planning, planning and documentation and collaboration with other organization (Wheatley and Hervieux, 2019). In addition, funding is considered to be a very important factor in implementing AI technologies in libraries.

AI & job fear

While it was felt that AI would improve the quality of library services, it has the potential to change the nature of jobs. The survey responses can be summarized in terms of three organizational cadres:

Top Management (No threat)

Middle Management (Low level of threat)

Ancillary Staff (High-risk threat)

As Wang (2017) has observed, older senior library staff tend not to be overly concerned about the impact of AI on their jobs, as they feel they will either have already retired or be close to retirement. Job threat perceived also mentioned (Cox et al., 2018). While respondents felt that middle management positions would still exist, they thought that the nature of those jobs would change. As already reported in the literature review, the respondents echoed current thinking that low cadre jobs would cease to exist as their tasks would have been totally automated. However, strategic investment on AI skills employee can retain in the job market (Rao, 2017).

AI challenges funding & technological issues

In the country like Pakistan investment in public and private sector universities are not up to the mark and libraries are also the neglected area regarding the adoption of the Technology. Libraries are always on top to cut off the funds if any shortfall of budget or funding crisis. Most of the Head librarian agreed with the funding issue has the main problem to adopt the AI-related tools and technologies.

However, three (30%) librarian agreed that Technological issues are the main barrier to implement AI in the libraries i.e. computer laptop tablet and Mobile phone processor, internet connection and speed, operate to hardware and software designed under the framework of AI. In the AI implementation in libraries technological tools reach slow and steady speed as indicated (Wheatley and Hervieux, 2019).

Conclusion

In Pakistan, there is a reasonable level of awareness among chief university librarians about artificial intelligence. Some AI-based technologies are already being used and include natural language processing, pattern recognition, and text data mining. However, technologies being introduced internationally, such as robotics and chatbots, have yet to be deployed in Pakistani academic libraries. In addition, there has been no effort thus far to address the challenges of big data other than at the national library level.

Academic libraries have an excellent opportunity to collaborate with other key stakeholders to advance the wider development of AI within their respective institutions. For example, they could initiate a conversation with any institutes and/or departments which have established an AI hub. They could collaborate with computer science departments to co-sponsor activities which create greater awareness more broadly of AI, e.g. speakers and workshops. Within the LIS profession, academic libraries could work collaboratively to encourage LIS schools to update their curriculum to include more in-depth coverage of AI technologies.

Ultimately, libraries can take a reactive approach to the foreshadowed greater integration of AI within their products and services or they can choose to adopt a proactive approach by examining how they can engage strategically with an AI-dominant future.

Footnotes

Acknowledgement

The authors acknowledge Dr. Joanna Richardson and Dr. Luke Tredinnick for their valuable comments on this paper, the interview participants who took part in this study, and the reviewers for their constructive feedback.

Authors’ note

This paper forms part of a PhD pilot study which examines the views of academic librarians working in the different universities of Karachi, Pakistan, about AI tools and the potential impact of their wider adoption in Pakistan. This data has been collected as part of the initial phase of the PhD study.

Declaration of conflicting interests

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

Funding

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

ORCID iD

Muhamamd Yousuf Ali

References

Ahmad

JianMing

Rafi

(2019) An analysis of academic librarians competencies and skills for implementation of Big Data analytics in libraries. Data Technologies and Applications 53(2): 201–216.

Ali

(2017) Library book theft and audits in university libraries of Pakistan. Journal of Library Administration 57(1): 87–98.

Ali

Haider

(2016) An analysis of digital reference services tools and usage in university libraries of Karachi. Pakistan Library & Information Science Journal 47(1): 20–29.

Aradau

Blanke

(2015) The (Big) Data-security assemblage: knowledge and critique. Big Data & Society 2(2): 2053951715609066.

Arlitsch

Newell

(2017) Thriving in the age of accelerations: a brief look at the societal effects of artificial intelligence and the opportunities for libraries. Journal of Library Administration 57(7): 789–798.

Asemi

(2018) Artificial intelligence (AI) application in library systems in Iran: a taxonomy study. Library Philosophy and Practice. 1840 . Available at: https://digitalcommons.unl.edu/libphilprac/1840 (accessed 28 December 2020).

Berendt

(2019) AI for the common good?! Pitfalls, challenges, and ethics pen-testing. Paladyn, Journal of Behavioral Robotics 10(1): 44–65.

Bohle

(2018) “Plutchik”: artificial intelligence chatbot for searching NCBI databases. Journal of the Medical Library Association: JMLA 106(4): 501–503.

Chen

Y-L

(2018) Development and evaluation of chatbot in National Taiwan Normal University Library. In: Proceedings of the 10th Asia Library and Information Research Group (ALIRG) workshop, Kyushu University, Fukuoka, Japan, 15–16 December 2018, pp. 1–6. Available at: http://opendata.kyushu-u.ac.jp/∼emi/alirg2018/wp-content/uploads/2018/12/4-6-Yi-LinChen.pdf (accessed 12 February 2020).

10.

Cox

Pinfield

Rutter

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

11.

Dwivedi

Hughes

Ismagilova

, et al. (2019) Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management.

12.

Edgcomb

Zima

(2019) Machine learning, natural language processing, and the electronic health record: innovations in mental health services research. Psychiatric Services 70(4): 346–349.

13.

Hilker

(1986) Artificial intelligence: a review of current information sources. Collection Building 7(3): 14–30.

14.

Jakhar

Kaur

(2020) Artificial intelligence, machine learning and deep learning: definitions and differences. Clinical and Experimental Dermatology 45: 131–132.

15.

Khan

Bhatti

(2018) Semantic Web and ontology-based applications for digital libraries: an investigation from LIS professionals in Pakistan. The Electronic Library 36(5): 826–841.

16.

Siau

(2018) Artificial intelligence impacts on higher education. In: Proceedings of the thirteenth Midwest Association for Information Systems conference, Vol. 42, pp. 1–5.

17.

Mahanty

Mahanti

(2019) Unleashing artificial intelligence onto big data: a review. In: Web Services: Concepts, Methodologies, Tools, and Applications. IGI Global, pp. 2099–2114.

18.

McCarthy

Minsky

Rochester

, et al. (1955) A proposal for the Dartmouth summer research project on artificial intelligence. Available at: http://www.formal.stanford.edu/jmc/history/dartmouth/dartmouth.html (accessed 1 November 2019).

19.

Mckie

IAS

Narayan

(2019) Enhancing the academic library experience with chatbots: an exploration of research and implications for practice. Journal of the Australian Library and Information Association 68(3): 268–277.

20.

Meincke

(2018) Experiences building, training, and deploying a chatbot in an academic library. Library Staff Publications. 28. Available at: https://scholarsarchive.jwu.edu/staff_pub/28 (accessed 27 February 2020).

21.

Müller

Bostrom

(2016) Future progress in artificial intelligence: a survey of expert opinion. In: Fundamental Issues of Artificial Intelligence. Cham: Springer, pp. 555–572.

22.

Paltoglou

Thelwall

(2012) Twitter, MySpace, Digg: unsupervised sentiment analysis in social media. ACM Transactions on Intelligent Systems and Technology 3(4): 66.

23.

Pang

Lee

(2008) Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1–2): 1–135.

24.

Phillips

(2017) Robots in the library: gauging attitudes towards developments in robotics and AI, and the potential implications for library services. Doctoral Dissertation, C. City University of London, UK.

25.

Popenici

Kerr

(2017) Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning 12(1): 22.

26.

Prats

Ramos-Garijo

Sanz

, et al. (2004) Recent progress in the UJI librarian robot. Available at: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.3048 (accessed 3 March 2020).

27.

Rao

(2017) A strategist’s guide to artificial intelligence. Strategy + Business Magazine, Vol. 87.

28.

Shah

Cappella

Neuman

(2015) Big data, digital media, and computational social science: possibilities and perils. The ANNALS of the American Academy of Political and Social Science 659(1): 6–13.

29.

Shahzad

Bilal

(2019) Twitter and Facebook usage for academic motivation among post-graduate students in Lahore, Pakistan. International and Public Affairs 3(1): 20–24.

30.

Siau

Yang

(2017) Impact of artificial intelligence, robotics, and machine learning on sales and marketing. In: Twelve annual Midwest Association for Information Systems conference (MWAIS 2017), pp. 18–19.

31.

Tredinnick

(2017) Artificial intelligence and professional roles. Business Information Review 34(1): 37–41.

32.

Tuarob

Bhatia

Mitra

, et al. (2016) AlgorithmSeer: a system for extracting and searching for algorithms in scholarly big data. IEEE Transactions on Big Data 2(1): 3–17.

33.

Wang

(2017) How do library staff view librarian robotics? Librarian staff’s ignored humanistic views on the impact and threat of robotics adoption. In: IFLA knowledge management section satellite conference, Corfu, Greece, 22 August 2017. Ionian University.

34.

Wheatley

Hervieux

(2019) Artificial intelligence in academic libraries: an environmental scan. Information Services & Use 39(4): 347–356.

35.

Wolfram

(2016) Bibliometrics, information retrieval and natural language processing: natural synergies to support digital library research. In: Proceedings of the joint workshop on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL), June 2016, pp. 6–13.

36.

Wood

Evans

(2018) Librarians’ perceptions of artificial intelligence and its potential impact on the profession. Computers in Libraries 38(1): 1–10.

37.

Williams

Chen

, et al. (2015) CiteSeerX: AI in a digital library search engine. AI Magazine, 36(3): 35–48.

38.

Yao

Zhang

Chen

(2015) Smart talking robot Xiaotu: participatory library service based on artificial intelligence. Library Hi Tech 33(2): 245–260.

39.

Ziemke

(2016) The body of knowledge: on the role of the living body in grounding embodied cognition. Biosystems 148: 4–11.