L’usage de l’intelligence artificielle générative dans les apprentissages universitaires : une revue systématique de la littérature

Manal LAMHARZI ALAOUI; Moulay Smail HAFIDI ALAOUI; Manuel BACHTOLD; Anita MESSAOUI; Houda ZOUIRCHI

doi:10.23882/ijdam.26346

Authors

Manal LAMHARZI ALAOUI Université Mohammed V, Rabat, Maroc
Moulay Smail HAFIDI ALAOUI Université Mohammed V, Rabat, Maroc
Manuel BACHTOLD Université de Montpellier & Université Paul Valéry Montpellier 3, France
Anita MESSAOUI Université de Montpellier & Université Paul Valéry Montpellier 3, France
Houda ZOUIRCHI HEC Business School, Rabat, Maroc https://orcid.org/0009-0000-1600-2527

DOI:

https://doi.org/10.23882/ijdam.26346

Keywords:

Intelligence artificielle générative, ChatGPT , pédagogie numérique, apprentissages universitaires , enseignement supérieur

Abstract

Generative artificial intelligence is experiencing rapid expansion in higher education and is progressively transforming university learning practices. Tools such as OpenAI and ChatGPT are now widely used by students and teachers for academic writing, information retrieval, document summarization, translation, and pedagogical assistance. In this context, this research proposes a systematic literature review aimed at analyzing the uses, benefits, limitations, and challenges associated with the integration of generative artificial intelligence into university learning practices. The methodology adopted is based on the PRISMA 2020 protocol recommendations. Scientific publications were collected from the Web of Science, Scopus, and Google Scholar databases. After applying the inclusion and exclusion criteria, a final corpus of articles was selected for thematic analysis. The findings reveal that generative artificial intelligence promotes personalized learning, student autonomy, improved academic productivity, and the development of new pedagogical practices. However, several risks also emerge, particularly those related to academic integrity, plagiarism, technological dependence, algorithmic bias, and the reliability of generated content.

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