Beyond digital access: Longitudinal pathways linking Generative AI and educational inequality in Vietnamese higher education

Description Overview This repository contains the dataset, questionnaires, analytical syntax, statistical outputs, and supplementary materials associated with the study: Beyond digital access: Longitudinal pathways linking Generative AI and educational inequality in Vietnamese higher education The study investigates longitudinal relationships among socioeconomic inequality, digital access conditions, Generative AI engagement, artificial intelligence literacy, self regulated learning, academic belonging, institutional support, and academic achievement within Vietnamese higher education environments. Research Context Generative AI technologies increasingly transform learning participation, academic interaction, and digital educational practices across higher education systems worldwide. Unequal technological conditions, however, continue creating differentiated opportunities for AI supported learning engagement among university students. Vietnamese higher education currently experiences rapid digital transformation alongside expanding institutional adoption of Generative AI technologies. Persistent socioeconomic inequality and unequal digital access conditions nevertheless remain important educational challenges across universities. This repository supports investigation of how technological access, AI literacy, and self regulated learning contribute to unequal educational participation and academic development within AI integrated learning environments. Dataset Information The repository includes a synthetic longitudinal dataset collected from: 1,326 undergraduate students six Vietnamese universities three longitudinal measurement waves The dataset contains variables related to: socioeconomic status digital access conditions Generative AI use artificial intelligence literacy self regulated learning academic belonging institutional AI support academic achievement All data are fully anonymized. Included Files Main Dataset Files genai_inequality_synthetic_Final.txt genai_inequality_synthetic_Final_Mplus.xlsx genai_inequality_synthetic_Final_Python.xlsx Documentation Files genai_inequality_synthetic_codebook.xlsx Questionnaires.docx Readme.txt Analysis Materials Code_Python_Mplus Results_Python_Mplus Model Visualization 000000000000_Model.jpg Measurement Information The study includes the following major constructs: Socioeconomic Status (SES) Digital Generative AI Access Conditions (DGAC) Generative AI Use (AIU) Artificial Intelligence Literacy (AILT) Self Regulated Learning (SRLG) Academic Belonging (ACBL) Institutional AI Support (INST) Previous GPA (PGPA) Final GPA (GPA) All latent constructs were measured using seven point Likert scales ranging from: 1 = Strongly disagree 7 = Strongly agree SES Calculation Socioeconomic Status (SES) was calculated using parental education level, household income, and reverse coded financial strain. (FINSTR_R = 8 − FINSTR) (SES_INDEX = (PEDU + INCOME + FINSTR_R) / 3) Higher SES_INDEX values indicate more favorable socioeconomic conditions. Analytical Procedures Data analysis was conducted using: Mplus 8.3 Python statistical procedures Analytical procedures included: Confirmatory Factor Analysis (CFA) Longitudinal Measurement Invariance Testing Structural Equation Modeling (SEM) Bootstrap Mediation Analysis Descriptive Statistical Analysis Missing data were handled using Full Information Maximum Likelihood estimation procedures. Ethical Statement Participation remained voluntary and anonymous throughout all longitudinal data collection waves. No personally identifiable information is included within the repository materials. The dataset and accompanying materials are shared exclusively for academic and research purposes. License This repository is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license unless otherwise specified.