EVALUATING THE EFFECTIVENESS OF AI-GENERATED FEEDBACK ON MEDICAL AND DENTAL STUDENTS' LEARNING OUTCOMES

Abstract

Background: Artificial Intelligence (AI) is increasingly being integrated into medical and dental education, particularly in providing automated feedback. However, its effectiveness in improving student learning outcomes remains underexplored.

Objective: This study evaluates the effectiveness of AI-generated feedback on the learning outcomes of medical and dental students at Karachi Medical and Dental College (KMDC).

Methods: A cross-sectional study was conducted among 310 students using a structured, self-administered questionnaire. The survey assessed demographic characteristics, prior exposure to AI tools, perceived effectiveness of AI-generated feedback, and self-reported improvements in learning outcomes. Statistical analyses, including chi-square tests and multivariate regression, were performed to determine associations between variables.

Results: The study found that 80% of students perceived AI-generated feedback as either highly or moderately effective. Similarly, 80% reported moderate to significant improvements in learning outcomes. Prior exposure to AI tools was significantly associated with a higher perceived effectiveness of AI-generated feedback (p = 0.012). However, differences based on the program of study (MBBS vs. BDS) and academic year were not statistically significant. Multivariate regression analysis confirmed prior AI exposure as a key predictor of perceived effectiveness (p = 0.004).

Conclusion: AI-generated feedback is perceived as an effective tool for enhancing learning outcomes among medical and dental students. Prior exposure to AI significantly influences students' perceptions, suggesting the need for broader AI integration in medical and dental curricula. Future research should explore long-term impacts and effectiveness across diverse educational settings.