Understanding dynamic learning profiles in AI-supported environments in Vietnam: Evidence from a three-wave latent transition analysis

This dataset accompanies the study “Understanding dynamic learning profiles in AI-supported environments in Vietnam: Evidence from a three-wave latent transition analysis.” It provides a comprehensive longitudinal dataset designed to examine how university students’ learning processes evolve in AI-supported environments over time. The dataset was collected from undergraduate students in Vietnam and follows a three-wave longitudinal design, including measurements at the beginning (T1), middle (T2), and end (T3) of a semester. The primary aim is to capture dynamic changes in key psychological and behavioral constructs related to AI-supported learning. The dataset includes three core constructs measured across all waves: AI self-efficacy, cognitive offloading, and metacognitive regulation. Each construct is operationalized using four Likert-scale items (1–7), allowing for both item-level and construct-level analyses. In addition, the dataset contains composite scores for each construct at each time point to support descriptive and auxiliary analyses. Background variables are also included, such as prior academic performance (GPA), gender, and field of study (STEM vs. non-STEM). Academic performance outcomes are captured through end-of-semester GPA, enabling analysis of the relationship between learning processes and performance. Missing data due to wave attrition are coded as 999, which is compatible with Mplus and other statistical software. The repository includes a structured codebook that provides detailed descriptions of all variables, along with Mplus syntax files and selected output files to support full reproducibility of the analyses. The analytical framework of the study includes descriptive statistics, measurement model evaluation, latent profile analysis (LPA) at each wave, and profile comparison analyses using covariates and academic outcomes. The dataset is particularly suited for researchers interested in longitudinal modeling, latent variable analysis, and the role of AI in higher education learning processes. This dataset is shared to promote transparency, reproducibility, and further research on AI-supported learning in higher education contexts, particularly in emerging educational systems such as Vietnam.