EFFECTIVENESS OF AI POWERED LEARNING PLATFORMS IN PUBLIC HEALTH EDUCATION

Abstract

Background: Artificial Intelligence (AI) has emerged as a transformative force in education, offering adaptive, personalized, and interactive learning experiences. In public health education—where interdisciplinary knowledge and real-world applicability are crucial—the effectiveness of AI-powered learning platforms requires empirical validation, particularly in low- and middle-income contexts.

Objective: To evaluate the effectiveness of AI-powered learning platforms in enhancing knowledge acquisition and learning outcomes in public health education among students in the Lahore region of Pakistan.

Methods: A quasi-experimental study was conducted over eight months with 260 undergraduate and postgraduate students, randomized into an AI-based intervention group (n=130) and a control group (n=130). Pre- and post-test knowledge assessments were conducted using a validated 60-item multiple-choice questionnaire. Student engagement and satisfaction were measured using structured Likert-scale surveys. Statistical analysis included paired and independent t-tests for within- and between-group comparisons.

Results: The AI group showed significantly greater improvement in knowledge scores (pre-test: 42.5 ± 6.2; post-test: 56.8 ± 5.9; p<0.001) compared to the control group (pre-test: 43.1 ± 6.4; post-test: 48.2 ± 6.1; p<0.001). Engagement (4.3 ± 0.6 vs. 3.5 ± 0.7), content satisfaction (4.2 ± 0.7 vs. 3.6 ± 0.8), and perceived learning (4.4 ± 0.5 vs. 3.7 ± 0.6) were also significantly higher in the AI group (p<0.001 for all). Knowledge gains exceeding 20% were achieved by 40.8% of the AI group compared to 9.2% in controls.

Conclusion: AI-powered learning platforms significantly enhance both cognitive and affective learning outcomes in public health education, offering a scalable, student-centered approach adaptable to diverse educational settings.