Nguyen Tat Trung; Quang Hung Do; Duc Trong Pham; Doan Thi Thanh Hang

AI-driven Psychographic and Behavioral Segmentation of Prospective University Students in Vietnam

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Author(s)

Nguyen Tat Trung ^1,2 Quang Hung Do ^3,* Duc Trong Pham ⁴ Doan Thi Thanh Hang ⁵

1. FPT University, Hanoi, 100000, Vietnam

2. Posts and Telecommunications Institute of Technology, Hanoi, 100000, Vietnam

3. Fintech Lab, Posts and Telecommunications Institute of Technology, Hanoi, 100000, Vietnam

4. Faculty of Information Technology, University of Labour and Social Affairs, Hanoi, 100000, Vietnam

5. Faculty of Information Technology, University of Transport Technology, Hanoi, 100000, Vietnam

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2026.03.01

Received: 22 Feb. 2026 / Revised: 8 Apr. 2026 / Accepted: 10 May 2026 / Published: 8 Jun. 2026

Index Terms

Higher Education Marketing, Psychographic Segmentation, Machine Learning, Technology Readiness, Student Recruitment, Vietnam

Abstract

The digital transformation of higher education marketing demands more sophisticated approaches to understanding prospective students beyond traditional demographic segmentation. This study develops a machine learning-based psychographic and behavioral segmentation framework for prospective university students in Vietnam, integrating constructs from consumer choice theory and technology adoption literature. We employ established unsupervised and supervised machine learning techniques (k-means clustering, Gaussian Mixture Models, and XGBoost classification) rather than claiming novel artificial intelligence architectures. Analyzing survey data from 1,486 Grade-12 students, our hybrid methodological approach identified three distinct segments: Intrinsically-Motivated Digital Explorers (27.7%), Prestige-Driven Traditionalists (38.9%), and Undecided Ambivalents (33.4%). Supervised learning (XGBoost) achieved 87.2% accuracy in predicting segment membership, with feature importance analysis revealing intrinsic motivation, technology readiness, and risk aversion as the primary discriminators. The findings extend higher education consumer choice theory by integrating technology readiness as an independent discriminative factor and demonstrate the methodological value of combining unsupervised and supervised machine learning for market segmentation.

Cite This Paper

Nguyen Tat Trung, Quang Hung Do, Duc Trong Pham, Doan Thi Thanh Hang, "AI-driven Psychographic and Behavioral Segmentation of Prospective University Students in Vietnam", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.3, pp. 1-20, 2026. DOI:10.5815/ijieeb.2026.03.01

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