# AI-enhanced assessment of psychological resilience: development and validation of a multidimensional psychological model in vocational college students

## TL;DR

This study created a multidimensional model to assess psychological resilience in vocational college students using AI and validated it with a large sample.

## Contribution

The study introduces an AI-enhanced, multidimensional resilience model with predictive and interpretable capabilities for educational settings.

## Key findings

- A three-factor resilience structure (tenacity, strength, optimism) was validated with strong psychometric properties.
- XGBoost outperformed logistic regression in predicting low resilience (AUC = 0.883).
- Sleep quality and perceived stress were identified as key predictors of resilience.

## Abstract

Accurate resilience evaluation is important to help vocational college students cope with transitional stress. This study developed and validated a multidimensional resilience framework using a “dual-track” design (N = 1,588). Psychometric analyses (Track A) revealed a robust 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 aligning with stress–resource theory. AI enhanced this by incorporating psychometrics and algorithms to give an accurate and explainable method for early identification of those in need of support in educational settings.