Abstract
Abstract
This study investigates AI-related technological proficiency among undergraduate students at the University of Casablanca and identifies, using a machine learning-based classification framework, the questionnaire items most strongly associated with this construct. Data were collected from 600 students across scientific and humanities disciplines using a validated 30-item questionnaire covering AI applications, AI-related skills, and improvement strategies. In addition to descriptive analysis, a classification approach was adopted to estimate construct-level proficiency categories from a reduced subset of questionnaire items to capture complex relationships between variables. To address a critical methodological challenge in questionnaire-based modeling, namely circularity and target leakage, the target variable was defined at the construct level, ensuring a clear separation from individual predictive items. Feature selection was performed exclusively on the training set, resulting in a subset of 17 informative features. Two classification models were developed and evaluated: SVC and GNB, using a 75/25 train-test split and repeated stratified tenfold cross-validation. In terms of construct-recovery accuracy, the degree to which the reduced-item model reproduces the construct-level proficiency category derived from the full 30-item instrument, the SVC reached 96.7% (AUROC = 0.9666), while GNB reached 94.7% (AUROC = 0.9560). Baseline models, including Logistic Regression (85%) and Decision Tree (88%), were also implemented to contextualize performance. The results confirm that the proposed models capture non-trivial patterns within the reduced item set and show stable performance across cross-validation folds (mean accuracy = 96.6%, SD = 0.9%), indicating that the selected model specification behaves consistently within this dataset rather than providing evidence of external predictive generalization. The findings indicate that a reduced subset of questionnaire items can closely approximate the construct-level proficiency category derived from the full 30-item instrument. Beyond empirical results, this approach contributes a reproducible, leakage-aware methodology for construct-level classification that can inform rather than independently drive measurement practices in higher education.