Artificial intelligence–assisted learning and standardized written examination performance among first-semester nursing and allied health diploma students: a cross-sectional census study

Background Artificial intelligence (AI) is increasingly integrated into health professions education; however, empirical evidence linking AI-assisted learning to objective academic performance remains limited. This study examined the level of AI-assisted learning and its association with standardized written examination performance among first-semester nursing and allied health diploma students. Methods A cross-sectional census study was conducted among 176 first-semester students at a Ministry of Health training institute. AI-assisted learning practices were measured using a validated 20-item questionnaire. Academic achievement was assessed using standardized final written examination scores (60%), comprising structured essay (40%) and multiple-choice questions (20%). Data were analysed using descriptive statistics, Pearson correlation, linear regression, and independent t-tests. Results Students reported high levels of AI-assisted learning engagement. However, no significant correlation was found between AI-assisted learning and examination performance (r = .087, p = .252). Regression analysis indicated that AI-assisted learning did not significantly predict examination scores (β = .087, R² = .008, p = .252). A significant difference in examination performance was observed between programmes (p = .003), although AI usage did not differ significantly. Conclusion While AI-assisted learning is widely adopted among health diploma students, its usage alone was not associated with improved standardized written examination performance. Structured pedagogical alignment and assessment design may be necessary to maximize the educational impact of AI integration in health professions education.