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Research on generative AI within university teaching contexts a methodological analysis

  • Ximena Coronado-Otavalo
  • , Viviana Galarza Ligna
  • , José Marcelino Romero-Gutiérrez
  • , Abel Romero

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This article presents the results of a systematic literature review aimed at identifying methodological trends in empirical and review studies concerning generative artificial intelligence (GenAI) within higher education institutions. The review was conducted in accordance with PRISMA guidelines, by using a corpus of peer-reviewed articles indexed in Scopus and Web of Science, published between 2021 and early 2024. Most studies are conducted within undergraduate and teacher education settings, with a primary focus on assessment processes and academic performance. A qualitative matrix was applied to 30 selected studies, considering variables such as type of research, methodological approach, educational level, data collection techniques, and ethical-pedagogical approach. The findings show the predominance of quantitative, cross-sectional designs based on surveys with limited validation procedures. The study concludes with an insight: the need to enhance methodological rigor and ethical alignment in future research on generative artificial intelligence within the university context.

    Original languageEnglish
    Title of host publicationETCM 2025 - 9th Ecuador Technical Chapters Meeting
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9798331552640
    DOIs
    StatePublished - 2025
    Event9th Ecuador Technical Chapters Meeting, ETCM 2025 - Quito, Ecuador
    Duration: Oct 21 2025Oct 24 2025

    Publication series

    NameETCM 2025 - 9th Ecuador Technical Chapters Meeting

    Conference

    Conference9th Ecuador Technical Chapters Meeting, ETCM 2025
    Country/TerritoryEcuador
    CityQuito
    Period10/21/2510/24/25

    Keywords

    • generative artificial intelligence
    • higher education
    • methodology
    • systematic literature review

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