Inteligencia artificial en educación superior: arquitecturas agénticas, feedback y enfoques pedagógicos emergentes. Una revisión de alcance 2024-2026

José Eugenio     Chafloque Capuñay; Elsa Dolores     Hernández García; Mariluz     Herrera Cervantes; Germán Vicente    Garay-Flores; Guadalupe   Badillo Márquez; Gladys    Huarachi Chuquimia

doi:10.47606/ACVEN/PH0588

Authors

José Eugenio Chafloque Capuñay Universidad Nacional Pedro Ruiz Gallo https://orcid.org/0009-0002-4642-1540
Elsa Dolores Hernández García Aden University – Panamá https://orcid.org/0009-0002-5839-2118
Mariluz Herrera Cervantes Universidad Pontificia de San Francisco Xavier de Chuquisaca – Bolivia https://orcid.org/0009-0002-3360-6247
Germán Vicente Garay-Flores Universidad César Vallejo – Perú https://orcid.org/0000-0002-7118-6477
Guadalupe Badillo Márquez Benemérito Instituto Normal del Estado "Gral. Juan Crisóstomo Bonilla": Puebla, México https://orcid.org/0000-0002-0364-9329
Gladys Huarachi Chuquimia Universidad Nacional Jorge Basadre Grohman, Perú https://orcid.org/0000-0003-3802-2574

DOI:

https://doi.org/10.47606/ACVEN/PH0588

Keywords:

agentic artificial intelligence, higher education, feedback, retrieval-augmented generation, multi-agent systems;, scoping review.

Abstract

Recent work on artificial intelligence in higher education suggests a shift from stand-alone generative tools toward more integrated configurations. This scoping review examined evidence published between 2024 and 2026, focusing on agentic architectures, feedback, personalization, pedagogical functions, and human oversight. Searches conducted on August 15, 2026, in Scopus, Web of Science, ERIC, SciELO, and Redalyc yielded 406 records. Following the selection process reported according to PRISMA-ScR, 44 studies were included for data extraction and synthesis. Six thematic axes were identified and subsequently organized into three dimensions: learning support, competencies and infrastructure, and implementation conditions. Agentic architectures showed greater technical explicitness, while personalization and feedback accounted for a substantial proportion of the pedagogical applications identified. However, this greater technical complexity was not accompanied by educational evidence at a comparable level of development. Gaps remain in deep learning, transfer, equity, and longitudinal evaluation, which continue to limit the assessment of sustained educational effects.

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