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Artificial Intelligence, data and analytics



**A Generative AI Primer. By Michael Webb. Jisc. 2 January 2024.

“Publishing an intro to generative AI is a challenge as things are moving so quickly. However, we think things have now settled down enough for us to bring together information in a single place, to create a short primer.” Table of contents

1. Introduction

2. An Introduction to the Generative AI Technology
2.1 ChatGPT
2.2. Microsoft Copilot
2.3. Google Bard
2.4. Other Models
2.5. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models
2.6 Image Generation
2.7 Beyond Chatbots
3. Impact of Generative AI on Education
3.1 Assessment
3.1.1 Guidance on advice to students
3.1.2 The role of AI detectors
3.2 Use in Learning and Teaching
3.2.1 Examples of use by students
3.2.2 Examples of use by teaching staff
3.2.3 Examples of uses to avoid
3.3 Adapting curriculum to reflect the use of AI in work and society.

4. Regulation

5. Summary

AI update from the Information Training Team by Phil Bradley**
Originally posted to the lis-link@JISCMAIL.AC.UK listserv on 27th November 2023. This is a useful update for librarians covering

* ChatGPT and other chatbots

* AI Search

* Content creation

Artificial Intelligence in academic library strategy in the United Kingdom and the Mainland of China. Yingshen Huang a, Andrew M. Cox b, John Cox C. The Journal of Academic Librarianship Volume 49, Issue 6, November 2023, There is growing recognition of the value of applying Artificial Intelligence (AI) in libraries. This study explores how academic libraries have responded to this opportunity at the level of strategy, what is the status of the application of AI, if any, and what are the different emphases of development comparing the UK and China. The data for the study was strategy documentation from high-ranking universities and their libraries. The sample consisted of the top 25 universities from the United Kingdom and top 25 from the Mainland of China according to the QS world university rankings. Explicit mention of Artificial Intelligence and related technologies is rarely found in strategic plans of universities in the UK but most Chinese universities mention them in their vision statements which focus on the development of new majors and research of the technology. Though several libraries have already implemented applications based on AI or claim to be “smart” or “intelligent” most academic library strategic plans or agendas do not emphasize AI. This is one of the first studies to explore the current status of AI applied in academic libraries as a sector and to compare experiences internationally.

The impact of Generative AI on libraries? Ex Libris [White paper] October 2023 Valid concerns and challenges surround Generative AI, leading to a growing debate on regulation. However, academic libraries cannot overlook its significant potential benefits. With an objective to deliver optimal service to users, libraries are looking at how their trusted vendors and suppliers may use Generative AI to help them achieve this goal.

Artificial Intelligence and Libraries Bibliography
Charles W. Bailey, Jr. Houston: Digital Scholarship, 2023
The Artificial Intelligence and Libraries Bibliography includes over 125 selected English-language articles and books that are useful in understanding how libraries are exploring and adopting modern artificial intelligence (AI) technologies. It covers works from January 2018 through August 2023

Library strategy and Artificial Intelligence . By Dr Andrew M Cox. [Blog] Understanding AI in Education [Jisc] National Centre for AI 5 June 2023 On April 20th 2023 the Information School, University of Sheffield invited five guest speakers from across the library sectors to debate “Artificial Intelligence: Where does it fit into your library strategy?”==== 23 resources to get up to speed on AI in 2023. Selected by the IFLA Artificial Intelligence SIG [Special Interest Group] Version 1 29/12/2022. IFLA

How artificial intelligence might change academic library work: Applying the competencies literature and the theory of the professions
Andrew Cox, Journal of the Association for information Science and Technology [JASIST]
07 March 2022 “The probable impact of artificial intelligence (AI) on work, including professional work, is contested, but it is unlikely to leave them untouched. The purpose of this conceptual paper is to consider the likelihood of the adoption of different approaches to AI in academic libraries.”

Understanding AI.

“To understand where AI should be used and will be most successful, one must understand what AI really is. AI, or machine learning, refers to a broad set of algorithms that can solve a specific set of problems, if trained properly”.

The success of artificial intelligence depends on data. Nick Ismail Information Age [blog] 23 April 2018

“The AI bucket consists of:

  • Big data
  • Analytics
  • Machine learning
  • Natural language processing
  • Data visualisation
  • Decision logic”

Cox, A.M. Pinfield, S. and Rutter, S. (2018) The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech. ISSN 0737-8831

Components of AI

“A composite including:
• Big data
• Analytics
• Machine learning
• Natural language processing
• Data visualisation
• Decision logic”

Smith, A. (2016). Big Data Technology, Evolving Knowledge Skills and Emerging Roles. Legal Information Management, 16(4), 219-224.

Common AI Terms

(Taken from:) AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence.Report of Session 2017–19 HL Paper 100 16 April 2018.


A series of instructions for performing a calculation or solving a problem, especially with a computer. They form the basis for everything a computer can do, and are therefore a fundamental aspect of all AI systems.

Expert system

A computer system that mimics the decision-making ability of a human expert by following pre-programmed rules, such as ‘if this occurs, then do that’. These systems fuelled much of the earlier excitement surrounding AI in the 1980s, but have since become less fashionable, particularly with the rise of neural networks.

Machine learning

One particular form of AI, which gives computers the ability to learn from and improve with experience, without being explicitly programmed. When provided with sufficient data, a machine learning algorithm can learn to make predictions or solve problems, such as identifying objects in pictures or winning at particular games, for example.

Neural network

Also known as an artificial neural network, this is a type of machine learning loosely inspired by the structure of the human brain. A neural network is composed of simple processing nodes, or ‘artificial neurons’, which are connected to one another in layers. Each node will receive data from several nodes ‘above ’it, and give data to several nodes ‘below’ it. Nodes attach a ‘weight’ to the data they receive, and attribute a value to that data. If the data does not pass a certain threshold, it is not passed on to another node. The weights and thresholds of the nodes are adjusted when the algorithm is trained until similar data input results
in consistent outputs.

Deep learning

A more recent variation of neural networks, which uses many layers of artificial neurons to solve more difficult problems. Its popularity as a technique increased significantly from the mid-2000s onwards, as it is behind much of the wider interest in AI today. It is often used to classify information from images, text or sound

AI in the UK: ready, willing and able? HOUSE OF LORDS Select Committee on Artificial Intelligence
Report of Session 2017–19 HL Paper 100 16 April 2018.

Adopting AI

“Adopting AI …is a journey, not a silver bullet that will solve problems in an instant. It begins with gathering data into simple visualizations and statistical processes that allow you to better understand your data and get your processes under control. From there, you’ll progress through increasingly advanced analytical capabilities, until you achieve that utopian goal”

Data Is the Foundation For Artificial Intelligence And Machine Learning, By Willem Sundblad Forbes [magazine] 18 October 2018

Why library analytics is on the rise

“Hierarchy of analytics use in libraries
Level 1 - Any analysis done is library function specific. Typically ad-hoc analytics but there might be dashboard systems created for only one specific area (e.g. collection dashboard for Alma or web dashboard for Google analytics)
Level 2 - A centralised library wide dashboard is created covering most functional areas in the library
Level 3 - Library “shows value” runs correlation studies etc
Level 4 - Library ventures into predictive analytics or learning analytics
By the time you reach level 4, it would be almost impossible for the library to go it alone”.

5 reasons why library analytics is on the rise. Aaron Tay. Musings about librarianship [blog]

How customer behaviour can drive intelligent library decision making

“Usage data on their own…give libraries and publishers very little insight into how content is being used or how much it is being looked at.
In spite of the huge amount of data that are now available to libraries, it feels as if little progress has been made in developing metrics that may give an indication of how resources are being used and the extent to which library users value the resources provided. These perceived shortcomings in conventional usage data led Nottingham Trent University and Alexander Street to partner in piloting an in-depth view of analytics, demonstrating user engagement and impact of use”.

Adey, H., & Eastman-Mullins, A. (2017). User engagement analytics case study: how customer behaviour can drive intelligent library decision making. Insights, 30(3), 138–147. DOI:

Learning analytics in higher education

“Every time a student interacts with their university – be that going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. Learning analytics is the process of using this data to improve learning and teaching”

Learning analytics in higher education: A review of UK and international practice. Read our updated briefing on learning analytics and student success from January 2017. By Niall Sclater, Alice Peasgood & Joel Mullen. Jisc. 2016.

The potential of data and learning analytics in Higher Education

The Higher Education Commission launched its fourth inquiry report, From Bricks to Clicks - The Potential of Data and Analytics in Higher Education, on 26 January 2016.

This report undertakes a review of the current data landscape across English higher education institutions, looking at data collections, learning analytics and the current barriers to implementing better data management and data analytics. It then looks ahead to how the HE sector may change in the next 5-10 years, how institutions can take advantage of the exciting opportunities that greater engagement with data and analytics offer, and how HE students stand to benefit.

The report finds that data analytics has the potential to transform the higher education sector, but cautions that UK institutions are currently not making the most of the opportunities in this area. It makes a series of recommendations targeted at the sector to help them take advantage of these opportunities and prepare for the data-driven future of higher education.

“We recommend that all institutions should consider introducing an appropriate learning analytics system to improve student support and performance”.

From bricks to clicks. The potential of data and learning analytics in Higher Education. Higher Education Commission 2016.

Strategic importance of AI in HE

“Artificial intelligence has opened up strategic opportunities for tertiary institutions to fundamentally redesign their businesses to deliver premium personalized services. Education CIOs can learn how virtual personal assistants provide an important building block toward that objective.”

Use AI to Take Student Success to the Next Level of Personalization in Higher Education. Nick Ingelbrecht & Jan-Martin Lowendahl. Gartner [report]. 14 February 2018

Using Student data for educational analysis

“Northumbria University’s approach to the utilisation of Educational Analytics is directly linked to the University Strategy.
In the future, the use of Educational Analytics may be extended to personalised earning paths, adaptive learning, personalised feedback, visualisations of study journey, intelligent e- tutoring, intelligent peer support, etc. Furthermore, new technological innovations might allow for more targeted, measured approaches.
The following data, which is currently captured by the University, is initially in scope
for Educational Analytics:
• personal information provided by the student at registration
• student level study records held by the University including assessment marks
• details of a student’s assigned Personal Tutor system-generated data from Blackboard, such as the date and frequency of accessing pages
• student attendance data
• library borrowing logs
• smart card activity log on Campus
• Northumbria gym membership
This data will be used in line with the University’s Student and Applicant Privacy Notice”.

Using Student data for educational analysis. Northumbria University. August 2018

Helping Research

“Yewno’s mission is ‘Knowledge Singularity’ and by that we mean the day when knowledge, not information, is at everyone’s fingertips. In the search and discovery space the problems that people face today are the overwhelming volume of information and the fact that sources are fragmented and dispersed. There’s a great T.S. Eliot quote ‘Where’s the knowledge we lost in information’ and that sums up the problem perfectly.”

Ruth Pickering. Chief Business Development & Strategy Officer. Do You Know About Yewno? By ALICE MEADOWS 7 JUN 2017

Making research more discoverable

“Content is at the centre of everything a publisher does. Enriching that content delivers significant value across the whole content life cycle. One particular area where the need for content enrichment can add significant value is in enabling the researcher to find and discover the most relevant content to assist in the researcher's workflow. Features that can be enhanced using enrichment techniques are relating articles, subject and context navigation, categorisation of content and identification of entities to provide linking to other relevant content”.

Content Enrichment Industry Insights. Number 1 (2016). 67 Bricks.

“The IET looked to Ontotext to deliver artificial intelligence technologies into its database for discovering emerging trends and relationships. This technology gives customers both a deeper understanding of current developments and more value from the data they have contributed so much towards”.
Ontotext Press Release 24 August 2016

The future of AI and scholarly publishing

“Elsevier’s Dr. Jabe Wilson answers the question “What does 2018 hold for AI in publishing?”He points out that while many industries have shifted to digital, the impact is especially dramatic in scientific publishing and R&D “due to the sheer volume of data researchers must sift through.” In fact, the desire to help researchers make sense out of all this data is behind Elsevier’s transformation from publisher to “information analytics provider.”

On the future of AI and scholarly publishing. R&D Magazine features Elsevier’s Consulting Director for Text and Data Analytics. By Alison Bert, DMA March 8, 2018 .

artificial_intelligence.txt · Last modified: 2024/02/27 08:59 by paul