Patterns and Predictors of Student Technological Proficiency in Heis: A Validated Instrument and Machine Learning Analysis

Abstract

This study examines AI-related technological proficiency among undergraduate students at the University of Casablanca and identifies the most informative indicators for prediction. Using a validated 30-item instrument covering AI applications, AI-related skills, and improvement strategies, data were collected from 600 students drawn from science and humanities programs. Overall proficiency was moderate: 63.3% of respondents met the predefined threshold, and significant group differences were observed by gender and academic specialization. For predictive modeling, correlation-based feature selection retained 17 high-value items. Two classifiers were then trained and evaluated using a 75/25 hold-out split, complemented by repeated stratified 10-fold cross-validation to assess stability. The Support Vector Classifier achieved 96.7% test accuracy with AUROC = 0.9666, while Gaussian Naïve Bayes reached 94.7% accuracy with AUROC = 0.9560; cross-validated estimates remained consistent with these results, supporting robustness. These findings indicate that a reduced set of questionnaire items can provide reliable estimates of students’ AI-related technological proficiency and can support scalable assessment and targeted interventions in higher education.