Adaptive and Personalized Learning in Higher Education

An Artificial Intelligence-Based Approach

Dados Bibliográficos

ID22047876
AutoresJuan Roberto Hernández-Herrera (0000-0001-9019-6243, Universidad Latina de América), Jesus Ortiz-Bejar (Universidad Michoacana de San Nicolás de Hidalgo), Jose Ortiz-Bejar (0000-0001-8023-8008, Universidad Michoacana de San Nicolás de Hidalgo)
Ano2026
Volume16
Fascículo1
Páginas109
Data de publicação2026-01-12
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEducation Sciences (JOURNAL)
Identificadores do periódicoISSN: 2227-7102 • E-ISSN: 2227-7102
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci16010109
OpenAlexW7123350411
IdiomaEN
Referências citadas38

The integration of Artificial Intelligence (AI) in higher education offers a potential solution to the scalability of personalized learning, yet empirical frameworks connecting diagnostic data with teacher-mediated interventions remain limited in developing contexts. This study adopts a sequential multi-phase research design to address this gap. Phase 1 comprised a diagnostic quantitative analysis of the National Survey on Access and Permanence in Education (ENAPE 2021), involving a representative sample of 3422 Mexican undergraduate students. Using Exploratory Factor Analysis (KMO = 0.96) and Pearson correlations, the study established a structural baseline. Phase 2 implemented a quasi-experimental exploratory pilot (N = 23) across two academic clusters (Civil Engineering and Nutrition) using “ActivAI”, a custom GPT configured with Retrieval-Augmented Generation (RAG). Results from Phase 1 revealed a strong, statistically significant correlation (r=0.72, p

Discipline · Empirical research · Exploratory factor analysis · Exploratory research · Higher education · Perception · Personalization · Scalability · Artificial Intelligence in Healthcare and Education · E-Learning and COVID-19 · Online Learning and Analytics