Examining the domains of AI-assisted learning and their relationship with academic performance among nursing and allied health students: a cross-sectional study

Background: Artificial intelligence (AI) is increasingly integrated into health professions education, yet its impact on academic performance remains unclear. While previous studies have examined overall AI usage, limited research has explored how specific domains of AI-assisted learning influence student outcomes. Methods: A cross-sectional study was conducted among 176 first-semester nursing and allied health students at a training institute in Malaysia. A structured questionnaire assessed four domains of AI-assisted learning: cognitive, efficiency, academic, and adaptive. Academic performance was measured using standardized examination scores. Data were analyzed using descriptive statistics, Pearson correlation, and multiple linear regression. Results: Students reported high levels of engagement across all AI-assisted learning domains. However, Pearson correlation analysis revealed no significant relationship between AI domains and academic performance (p > 0.05). Multiple regression analysis further confirmed that none of the AI domains significantly predicted academic performance (R² = 0.021, p = 0.453). Conclusion: Despite widespread use of AI-assisted learning, no significant association with academic performance was observed. These findings suggest that while AI may support perceived learning, its effectiveness in improving measurable academic outcomes remains uncertain. Further research is needed to explore how AI can be integrated to enhance higher-order learning.