Institutional Approaches to Generative AI in Higher Education: Guided Use versus Prohibition and Their Associations with Learning, Plagiarism, and the Quality of Academic Work
DOI:
https://doi.org/10.70577/asce.v5i1.638Keywords:
Generative AI; Higher education; Academic integrity; Plagiarism; Assessment; academic writing.Abstract
The rapid diffusion of generative AI in higher education has intensified tensions between pedagogical innovation and academic integrity, since large language models can produce text and solutions that appear competent, requiring institutions to rethink assessment, authorship, and evidence of learning. This study systematically analyzes evidence published between 2020 and 2025 on institutional approaches to generative AI, contrasting guided use, defined as criteria-based tasks, responsible prompting, disclosure and citation of AI support, and verification practices, versus total bans or unguided use. The review was conducted through a structured search in SCOPUS, Web of Science, SciELO, and Google Scholar, selecting thirty open-access articles, with systematic extraction of study design, population, disciplinary context, outcomes, and effect estimates when available. Results indicate that guided use is associated with improvements in performance and the quality of academic work, particularly in writing-intensive courses and project-based learning, whereas plagiarism and integrity outcomes are more strongly linked to policy clarity, assessment redesign, and AI literacy than to absolute prohibitions. The evidence supports governance models that prioritize transparency, verification, and process-focused assessment, reducing reliance on automated detection and strengthening conditions for authentic learning.
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Copyright (c) 2025 Nixon Santiago Fonseca Loya, Tania Gabriela Quishpe Gonsalez, Evelyn Magaly Castillo Malquin, Marcia Soledad Carvajal Bautista, Gabriela Andrea Ramos Cáceres
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