The impact of AI integration on curriculum development in Vietnamese higher education during educational reform

Dataset Description

This dataset captures the relationships between artificial intelligence integration, curriculum development processes, and student academic performance within Vietnamese higher education under policy-driven reform conditions. The data were collected from 277 academic staff members who are directly involved in curriculum design, program evaluation, and instructional development across multiple higher education institutions.

The dataset is structured to examine how artificial intelligence functions as an institutional and pedagogical driver influencing curriculum transformation through several interconnected mechanisms. The conceptual framework includes seven latent constructs: AI integration, change in career outlooks, content integration, pedagogical approaches, evaluation and assessment methods, interdisciplinary focus, and student academic performance. Each construct is operationalized through multiple reflective indicators measured using Likert-type scales.

Responses reflect professional judgments based on participants’ experience with curriculum implementation and exposure to artificial intelligence in academic contexts. The dataset represents a cross-sectional design and provides insight into how institutional expectations, technological adoption, and curriculum structures interact to shape perceived academic outcomes.

The data support structural equation modeling, particularly Partial Least Squares Structural Equation Modeling, allowing simultaneous estimation of measurement and structural relationships. The dataset enables analysis of direct, indirect, and mediating effects linking artificial intelligence integration with student academic performance through curriculum mechanisms.

The dataset is suitable for research on educational technology integration, curriculum design, higher education reform, and artificial intelligence in learning environments. Interpretation of results should consider that academic performance is measured through professional perception rather than objective student-level performance indicators, which may introduce response-related variability.