Exploring uses of AI in libraries

From discovery tools to content digitization, librarians are exploring the uses of AI in libraries. But seamless access to original content will remain critical

In recent years, artificial intelligence (AI) and machine learning have become buzzwords, driven by the popularity of chatbots such as ChatGPT.

So at this year’s Access Lab, speakers and panelists discussed the uses of AI in libraries - alongside the drawbacks.

Keynote speaker Dr Luba Pirgova-Morgan, research fellow at the University of Leeds in the UK, said research shows AI can be like a “library superhero”. And she also said it’s important to address risks, which include privacy and bias.

So what are the uses of AI in libraries? Here are some areas for libraries to keep an eye on.

Front-end discovery

At Access Lab, Bella Ratmelia from Singapore Management University (SMU) libraries introduced delegates to a range of discovery tools:

• Elicit, which generates written responses to queries, backed up by citations from the Semantic Scholar database
• Consensus, which searches Semantic Scholar and ranks the “consensus” behind its proposed response
• SciSpace, which combines a literature review with functions such as an APA citation generator
• ResearchRabbit, which helps users map relationships between research, typically using “mind map” visualizations.

And according to a report presented by Dr Pirgova-Morgan, VOSviewer – a similar concept to ResearchRabbit – is used at the University of Leeds as part of its literature search services.

Users should, of course, check the accuracy of AI-generated results. This means that seamless access to original content is critical.

Libraries may also seek AI models that interact with publishers’ own datasets – or libraries’ own collections.

Content management and digitization

Other AI technologies can help libraries to digitize and analyze content, such as offline collections.

A 2021 survey by the Conference of European National Librarians (CENL), cited by Dr Pirgova-Morgan’s report Looking towards a bright future found libraries are working on tools to do this. These include layout analysis, handwritten text recognition (HTR), and optical character recognition (OCR).

At Access Lab, Dr Pirgova-Morgan told how AI helps the University of Leeds to digitize ancient texts.

“They're using AI tools to actually upload images of ancient texts, and pull apart the text from the white spaces,” Dr Pirgova-Morgan said.

The CENL survey also cites automated cataloging, which could help with de-duplicating library records. This is otherwise a tedious manual job.

Other applications of AI in libraries

At Access Lab, Dr Pirgova-Morgan discussed how AI tools could “streamline library operations, automating mundane tasks.”

The University of Leeds, for example, is looking at how AI could support features such as booking systems for its learning development programs.

Panelist Rosalia da Garcia from Sage Publishing, who studies at Stanford University, also spoke about how a virtual reality classroom and chatbot coaching can enhance the student experience.

With so many options, what are the future uses of AI in libraries? Perhaps libraries will evolve, but remain true to their purpose of promoting access to knowledge.

If risks to privacy, accuracy and fairness are managed, the future of AI in libraries could be bright.

Watch our Access Lab AI playlist to hear the full discussion.

[A full version of this article was published in Research Information on 11 July 2024.]