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ARCHIVING WITH AI.

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Bibliographic Details
Title: ARCHIVING WITH AI.
Authors: Enis, Matt
Source: Library Journal. Jun2026, Vol. 151 Issue 6, p30-33. 4p. 4 Color Photographs.
Subject Terms: *Artificial intelligence, *Libraries, *Archives, *Digitization, Archival materials
Company/Entity: University of Virginia
Abstract: The article discusses how artificial intelligence (AI) companies are partnering with libraries and archives by offering funding for digitization projects. Topics include a predictable consequence if libraries allow generative AI to ingest archival materials as training data without requiring provenance conditions, purpose of the Archival AI Protocol established by University of Virginia (UVA), and the role of Retrieval-Augmented Generation (RAG) in refining AI outputs.
Database: Education Research Complete
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Header DbId: ehh
DbLabel: Education Research Complete
An: 193895734
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
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  Data: ARCHIVING WITH AI.
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  Data: <searchLink fieldCode="AR" term="%22Enis%2C+Matt%22">Enis, Matt</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Library+Journal%22">Library Journal</searchLink>. Jun2026, Vol. 151 Issue 6, p30-33. 4p. 4 Color Photographs.
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  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Libraries%22">Libraries</searchLink><br />*<searchLink fieldCode="DE" term="%22Archives%22">Archives</searchLink><br />*<searchLink fieldCode="DE" term="%22Digitization%22">Digitization</searchLink><br /><searchLink fieldCode="DE" term="%22Archival+materials%22">Archival materials</searchLink>
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  Data: The article discusses how artificial intelligence (AI) companies are partnering with libraries and archives by offering funding for digitization projects. Topics include a predictable consequence if libraries allow generative AI to ingest archival materials as training data without requiring provenance conditions, purpose of the Archival AI Protocol established by University of Virginia (UVA), and the role of Retrieval-Augmented Generation (RAG) in refining AI outputs.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=193895734
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 4
        StartPage: 30
    Subjects:
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Libraries
        Type: general
      – SubjectFull: Archives
        Type: general
      – SubjectFull: Digitization
        Type: general
      – SubjectFull: Archival materials
        Type: general
      – SubjectFull: University of Virginia
        Type: general
    Titles:
      – TitleFull: ARCHIVING WITH AI.
        Type: main
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            NameFull: Enis, Matt
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 03630277
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              Value: 151
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              Value: 6
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            – TitleFull: Library Journal
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