Inteligencia artificial generativa y escritura académica en la educación superior: revisión integrativa de sus implicaciones pedagógicas, éticas y evaluativas

José Eugenio    Chafloque-Capuñay; Marcela Beatriz    Uría-Sánchez; Eduardo Abrahan G    Gonzales-Velásquez; Arnulfo    Borges-Huanca; Abel     Marocho-Rojas; Yovert Edwin     Ussa-Lopez

doi:10.47606/ACVEN/PH0487

Authors

José Eugenio Chafloque-Capuñay Universidad Nacional Pedro Ruíz Gallo-Perú https://orcid.org/0009-0002-4642-1540
Marcela Beatriz Uría-Sánchez Universidad Nacional Federico Villarreal-Perú https://orcid.org/0000-0002-2807-2036
Eduardo Abrahan G Gonzales-Velásquez Universidad Cesar Vallejo- Perú https://orcid.org/0000-0003-1767-0089
Arnulfo Borges-Huanca Universidad San Francisco Xavier- Bolivia https://orcid.org/0009-0003-7087-2349
Abel Marocho-Rojas CAEN-EPG- Perú https://orcid.org/0009-0003-3224-5045
Yovert Edwin Ussa-Lopez Investigador Independiente- Perú https://orcid.org/0009-0004-1529-7297

DOI:

https://doi.org/10.47606/ACVEN/PH0487

Keywords:

generative artificial intelligence, academic writing, higher education, critical literacy, academic assessment

Abstract

The incorporation of generative artificial intelligence into higher education has rapidly transformed academic writing practices, creating pedagogical opportunities as well as ethical and assessment-related challenges. In this context, the aim of this article is to present an integrative literature review based on a critically oriented thematic synthesis in order to analyze how recent research addresses the pedagogical, ethical, and evaluative implications of using these tools in university academic writing. The search was conducted in Scopus, ERIC, and SciELO, considering publications in Spanish, English, and Portuguese published between 2022 and March 2026. After the selection process, a corpus of 21 studies was established. The analysis identified four main axes: pedagogical integration of generative AI, student agency and critical literacy, ethics, authorship and academic transparency, and assessment in AI-mediated contexts. The reviewed evidence suggests that these technologies can expand support for text planning, revision, and feedback, but they also raise risks related to instrumental dependence, weakening of authorial voice, and opacity in academic production processes. It is concluded that their integration into university writing requires explicit pedagogical approaches, clear ethical criteria, and assessment models focused on process, traceability, and reflective authorship.

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