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From Card Catalogs to Semantic Search: Building a Human-Centered Discovery Platform Powered by AI Technologies.

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Title: From Card Catalogs to Semantic Search: Building a Human-Centered Discovery Platform Powered by AI Technologies.
Authors: Caizzi, Carolyn1 ccaizzi@berkeley.edu, Deschenes, Amy2 amy_deschenes@harvard.edu
Source: Information Technology & Libraries. Mar2026, Vol. 45 Issue 1, p1-23. 23p.
Subject Terms: *Bibliographic databases, *Diffusion of innovations, *Artificial intelligence, *Information retrieval, *Metadata, *Machine learning, Medical information storage & retrieval systems, Self-efficacy, Natural language processing, Medical subject headings, Trust, Semantics, Integrated library systems (Computer systems), User interfaces, Chatbots
Abstract: The first phase of the Reimagining Discovery project at Harvard Library sought to address the challenge of fragmented search experiences of special collections materials using artificial intelligence (AI) technologies, such as embedding models and large language models (LLMs). The resulting platform, Collections Explorer, simplifies and enhances the search experience for more effective special collections discovery. The project team took a user-centered and trustworthy approach to implementing AI, grounding the choices of the platform in user empowerment and librarian expertise. The development process included extensive user research, including interviews, usability testing, and prototype evaluations, to understand and address user needs. Collections Explorer was developed using a multi-component architecture that integrates multiple types of AI. The team evaluated more than 12 models to select ones that were the best fit for the need, as well as being ethical and sustainable. Detailed system prompts were developed to guide LLM outputs and ensure the reliability of information. The methodical and iterative approach helped to create a flexible and scalable platform that could evolve to support other material types in the future. Initial research showed that potential users are enthused at the prospect of AI-powered features to enhance discovery, especially the item-level summaries and related search suggestions. The project demonstrated the potential of integrating AI technologies into library discovery systems while maintaining a commitment to trustworthiness and user-centered design. [ABSTRACT FROM AUTHOR]
Copyright of Information Technology & Libraries is the property of American Library Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: From Card Catalogs to Semantic Search: Building a Human-Centered Discovery Platform Powered by AI Technologies.
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  Data: <searchLink fieldCode="AR" term="%22Caizzi%2C+Carolyn%22">Caizzi, Carolyn</searchLink><relatesTo>1</relatesTo><i> ccaizzi@berkeley.edu</i><br /><searchLink fieldCode="AR" term="%22Deschenes%2C+Amy%22">Deschenes, Amy</searchLink><relatesTo>2</relatesTo><i> amy_deschenes@harvard.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Information+Technology+%26+Libraries%22">Information Technology & Libraries</searchLink>. Mar2026, Vol. 45 Issue 1, p1-23. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Bibliographic+databases%22">Bibliographic databases</searchLink><br />*<searchLink fieldCode="DE" term="%22Diffusion+of+innovations%22">Diffusion of innovations</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br />*<searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+information+storage+%26+retrieval+systems%22">Medical information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Self-efficacy%22">Self-efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+subject+headings%22">Medical subject headings</searchLink><br /><searchLink fieldCode="DE" term="%22Trust%22">Trust</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+library+systems+%28Computer+systems%29%22">Integrated library systems (Computer systems)</searchLink><br /><searchLink fieldCode="DE" term="%22User+interfaces%22">User interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink>
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  Label: Abstract
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  Data: The first phase of the Reimagining Discovery project at Harvard Library sought to address the challenge of fragmented search experiences of special collections materials using artificial intelligence (AI) technologies, such as embedding models and large language models (LLMs). The resulting platform, Collections Explorer, simplifies and enhances the search experience for more effective special collections discovery. The project team took a user-centered and trustworthy approach to implementing AI, grounding the choices of the platform in user empowerment and librarian expertise. The development process included extensive user research, including interviews, usability testing, and prototype evaluations, to understand and address user needs. Collections Explorer was developed using a multi-component architecture that integrates multiple types of AI. The team evaluated more than 12 models to select ones that were the best fit for the need, as well as being ethical and sustainable. Detailed system prompts were developed to guide LLM outputs and ensure the reliability of information. The methodical and iterative approach helped to create a flexible and scalable platform that could evolve to support other material types in the future. Initial research showed that potential users are enthused at the prospect of AI-powered features to enhance discovery, especially the item-level summaries and related search suggestions. The project demonstrated the potential of integrating AI technologies into library discovery systems while maintaining a commitment to trustworthiness and user-centered design. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Information Technology & Libraries is the property of American Library Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.5860/ital.v45i1.17511
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Bibliographic databases
        Type: general
      – SubjectFull: Diffusion of innovations
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Information retrieval
        Type: general
      – SubjectFull: Metadata
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Medical information storage & retrieval systems
        Type: general
      – SubjectFull: Self-efficacy
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Medical subject headings
        Type: general
      – SubjectFull: Trust
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      – SubjectFull: Semantics
        Type: general
      – SubjectFull: Integrated library systems (Computer systems)
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      – SubjectFull: User interfaces
        Type: general
      – SubjectFull: Chatbots
        Type: general
    Titles:
      – TitleFull: From Card Catalogs to Semantic Search: Building a Human-Centered Discovery Platform Powered by AI Technologies.
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              Text: Mar2026
              Type: published
              Y: 2026
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