Automatic Classification of Subjects and Sustainable Development Goals (SDGs) in Documents with Generative AI: An Experience from the Unicamp Library System.

This study evaluates the effectiveness of the Artificial Intelligence for Theme Generation tool (original Portuguese acronym name: IAGeraTemas), developed with generative artificial intelligence (AI; Google Gemini), for automating thematic classification and the assignment of Sustainable Development...

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Publicado en:Information Technology & Libraries Vol. 45; no. 1; pp. 1 - 25
Autores principales: deu Gonçalves de Oliveira Foz, Francisco Ta, Martins, Márcio Souza, de Souza Neves, Alessandra Karyne Clemente, de Carvalho Mansur, Erica Cristina, Eliel, Oscar
Formato: pictorial research tables/charts Journal Article
Publicado: American Library Association Mar2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Mar2026
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      pub: American Library Association
      place: Chicago, Illinois
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        atl: Automatic Classification of Subjects and Sustainable Development Goals (SDGs) in Documents with Generative AI: An Experience from the Unicamp Library System.
      aug:
        au:
          deu Gonçalves de Oliveira Foz, Francisco Ta
          Martins, Márcio Souza
          de Souza Neves, Alessandra Karyne Clemente
          de Carvalho Mansur, Erica Cristina
          Eliel, Oscar
        affil: Project Manager, State University of Campinas (UNICAMP)
      sug:
        subj:
          Sustainable Growth Classification
          Environmental Sustainability Classification
          Thematic Analysis
          Artificial Intelligence, Generative
          Human
          Automation
          Libraries, Academic
          Librarians
          Abstracting and Indexing
          Quantitative Studies
          Qualitative Studies
      ab: This study evaluates the effectiveness of the Artificial Intelligence for Theme Generation tool (original Portuguese acronym name: IAGeraTemas), developed with generative artificial intelligence (AI; Google Gemini), for automating thematic classification and the assignment of Sustainable Development Goals (SDGs) in documents. The methodology combined quantitative analyses (metrics of precision, recall, and accuracy) on 50 articles published by authors from the State University of Campinas (Unicamp), using classification from the SciVal database and qualitative analyses (analysis of the relevance of terms indexed by librarians from the Unicamp Library System in 40 articles available in the Unicamp Institutional Repository), comparing them with manual indexing performed by librarians. The quantitative results in SDG classification showed a recall of 0.785, while the "precision" and "accuracy" metrics were moderate. The qualitative analysis deepened the evaluation of term coherence and relevance suggested by the AI versus human indexing. It revealed the tool's potential for suggesting relevant terms and expanding concepts, but it also exposed limitations in addressing complex topics. The research, conducted as an experiment at Unicamp Library System, concludes that IAGeraTemas is a valuable auxiliary tool, complementing but not replacing manual indexing, reinforcing the importance of human expertise in validating and refining results, and emphasizing the synergistic potential between AI and information professionals.
      pubtype: Academic Journal
      doctype:
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        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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