Artificial intelligence (AI) has become a buzzword across industries and is rapidly transforming the way organizations operate. The library industry is no exception, and libraries worldwide are integrating AI technologies to enhance their services and resources. Libraries are utilizing AI for several purposes, such as content indexing, enhancing discoverability, refining metadata, and machine learning. In this article, we will explore how libraries are utilizing AI for research purposes and how this technology can help them in their endeavors.

AI for Research: Examples from Libraries

  1. Content Indexing

    Content indexing is the process of assigning relevant descriptors or metadata to a document or piece of content to make it easier to search for and retrieve in a library system. Traditionally, this has been a time-consuming and labor-intensive task that required humans to read through each document and manually assign keywords and descriptors. However, with the advancements in AI and natural language processing (NLP), libraries are now able to automate this process to a large extent.

    AI algorithms can automatically extract and analyze relevant keywords, phrases, and topics from a document by using techniques such as machine learning, deep learning, and NLP. These algorithms can understand the context and meaning of the text, and can identify important concepts and topics even if they are not explicitly mentioned in the text. This allows libraries to efficiently and effectively retrieve information based on the user's search query.

    Libraries such as Harvard Library, MIT Libraries, and the National Library of Sweden have implemented AI for content indexing to improve the efficiency and accuracy of their search systems. By automating the indexing process, libraries can save time and resources while providing better search results to their users. Additionally, AI-based indexing can improve the discoverability of documents by uncovering connections and relationships between different pieces of content that might not be immediately apparent to a human indexer.

  2. Enhancing Discoverability

    To improve discoverability and user engagement, AI technologies can also assist libraries in collection development and preservation. Machine learning algorithms can analyze circulation and usage data to identify popular and valuable resources, which can inform collection development decisions. AI can also assist in preserving fragile and rare materials by automatically detecting and correcting errors in digitized copies of books and documents.

    Furthermore, AI can help libraries with tasks such as cataloging, metadata creation, and classification. By analyzing text and visual content, AI algorithms can automatically generate descriptive metadata and classify resources according to subject areas, genres, and other criteria. This can save time and reduce errors in the cataloging process.

    Overall, AI technologies have the potential to revolutionize the way libraries operate and serve their users. By automating tedious and manual tasks, AI can free up library staff to focus on higher-level tasks such as user engagement, outreach, and research support. However, it is important to ensure that AI is implemented ethically and transparently, with appropriate consideration given to issues such as bias, privacy, and data security.

  3. Refining Metadata

    Metadata refers to the descriptive information about a resource that enables users to locate and access it. However, the metadata of digital resources can be incomplete, inconsistent, or outdated, which can make it difficult for users to discover and access them. AI technologies can help libraries refine metadata by automatically analyzing the content of the resource and extracting relevant information.

    For example, AI algorithms can analyze an image and identify the objects, people, and places depicted in it, which can be used to create more descriptive metadata. Similarly, AI algorithms can analyze the content of an article or thesis and extract relevant keywords, topics, and concepts, which can be used to create more accurate and comprehensive metadata.

    Refining metadata using AI can also help libraries overcome the challenge of managing large and diverse collections. For example, the University of Michigan Library has over 1.3 million digitized items in its collections, which can be challenging to manage and make discoverable. However, by using AI to refine metadata, the library has been able to enhance the discoverability and accessibility of its digital collections.

  4. Machine Learning for Resource Management

    Machine learning algorithms can also be used to optimize the shelving and organization of physical resources in libraries. By analyzing user behavior, such as the frequency and duration of borrowing, machine learning can recommend the optimal placement of resources to improve accessibility and efficiency. This can save time and effort for library staff, as well as improve the overall user experience.

    Furthermore, machine learning can also be used to monitor and maintain the condition of physical resources, such as books and documents. For example, algorithms can analyze usage patterns and recommend repairs or replacements for damaged or frequently used items. This can help libraries extend the lifespan of their resources and ensure that they are in good condition for users.

    Overall, the use of machine learning for resource management in libraries can improve efficiency, enhance the user experience, and help libraries better allocate their resources.

In addition to the examples mentioned in the article, there are other ways libraries are utilizing AI for research purposes. For instance, AI can assist with text mining and analysis, allowing researchers to identify patterns and insights in large amounts of textual data. This can be particularly useful in fields such as social sciences and humanities, where researchers analyze large amounts of text-based data, such as interviews, surveys, and archives.

Furthermore, AI-powered recommendation systems can help researchers discover new resources and ideas relevant to their research interests. These systems analyze user behavior and preferences, such as the books they have borrowed or the articles they have read, to provide personalized recommendations for new resources to explore.

Another area where AI can be useful is in data management and curation. Libraries often have large amounts of research data stored in various formats, and managing and curating this data can be a challenge. AI can assist with tasks such as data classification, data cleaning, and data validation, ensuring that research data is organized and easily accessible for researchers.

Finally, AI can also be used for plagiarism detection, allowing libraries to identify instances of academic misconduct in research papers and other academic works. This can help ensure the integrity of academic research and uphold academic standards.

In summary, AI technologies offer several benefits to libraries for research purposes, including content indexing, enhancing discoverability, refining metadata, machine learning, text mining and analysis, recommendation systems, data management, and plagiarism detection. As libraries continue to explore new ways to leverage AI for research, it is essential to ensure that these technologies are implemented ethically and transparently, with appropriate consideration given to issues such as bias, privacy, and data security.


The integration of AI technologies in libraries is transforming the way libraries operate, enhancing their efficiency, and improving their user experience. Libraries are utilizing AI for several purposes, such as content indexing, enhancing discoverability, refining metadata, and machine learning. These examples demonstrate how libraries can leverage AI to enhance their research capabilities and serve their users better. The potential of AI for libraries is vast, and libraries worldwide are exploring new ways to utilize this technology for their benefit.