Mapping artificial intelligence integration in higher education: a systematic review using the FACETS and SAMR frameworks
Résumé fourni par la source
Background Artificial intelligence (AI) is reshaping higher education through applications in teaching, learning, assessment, and curriculum design. Despite growing adoption, the literature remains fragmented, lacking structured frameworks to evaluate AI's integration and impact. Objective This review aimed to map how AI has been integrated into higher education, classify applications using the SAMR model (Substitution, Augmentation, Modification, Redefinition), analyze reported outcomes and challenges, and apply the FACETS framework (Form, AI use case, Context, Education focus, Technology, SAMR) to enhance comparability across studies. Methods The review followed PRISMA 2020 guidelines and was registered using the PRISMA-P protocol. Eight databases (PubMed, Medline, Web of Science, ProQuest, Scopus, Dimensions, OpenAlex, IEEE Xplore) were searched for English-language articles published between January 2015 and July 2025. Eligible studies examined AI integration in undergraduate higher education. A total of 959 records were screened in Rayyan QCRI. After removing duplicates and exclusions, 22 studies met the inclusion criteria. Data extraction covered study design, discipline, AI tool, SAMR level, outcomes, and FACETS dimensions. Results Most included studies originated from North America and Asia, with medicine, computer science, and engineering as leading disciplines. ChatGPT, was the most common platform. Applications clustered around assessment automation and personalized learning support. Most implementations were at the Substitution or Augmentation levels, with fewer Modification and one Redefinition. Reported benefits included efficiency, personalization, and engagement, while challenges were equity, ethics, and academic integrity. Conclusion AI in higher education remains largely incremental, enhancing existing practices rather than transforming pedagogy. Its greatest potential lies in personalization.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Mapping artificial intelligence integration in higher education: a systematic review using the FACETS and SAMR frameworks
- Date Crossref
- 01/09/2026
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.
Institutions déclarées
Une affiliation ne permet pas de déduire la nationalité d’un auteur.