ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
This article is part of the Research TopicMultidimensional Responses to AI-Driven Transformation in Educational Contexts: Theoretical Frameworks, Tool Development, and Practical ExplorationView all 7 articles
AI-Enhanced Assessment of Psychological Resilience: Development and Validation of a Multidimensional Psychological Model in Vocational College Students
Provisionally accepted
- 1Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
- 2Nanchong Vocational and Technical College, nanchong, China
- 3Sichuan Institute of Arts and Science, sichuan, China
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Accurate resilience evaluation is important to help vocational college students deal with transitional stress. This study developed and validated a multidimensional resilience framework via a "dual-track" design (N=1,588). Psychometric Analyses (Track A) revealed a sound three-factor structure with Tenacity, Strength, and Optimism. Measurement Invariance across genders was demonstrated. Using machine learning for predictive validation (Track B), it was found that the XGBoost model performed better (AUC = 0.883) in predicting low-resilience risk than the traditional logistic regression model. Interpretability analysis through SHAP highlighted sleep quality and perceived stress as key predictors matching stress-resource theory. AI enhanced this with the addition of psychometrics and algorithms to give an accurate and explainable method of identifying who needs support early in education settings.
Keywords: MachineLearning3, Psychological resilience1, psychometric validation4, risk stratification5, Vocational college students2
Received: 22 Dec 2025; Accepted: 10 Feb 2026.
Copyright: © 2026 Xu, Cui, Zhao, Yang, Yang, Huang and Mustapha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jiajia Xu
Mazni Mustapha
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