ARTIFICIAL INTELLIGENCE IN MEDICAL EDUCATION: A SYSTEMATIC REVIEW OF ITS BENEFITS AND DRAWBACKS ON STUDENT LEARNING

Abstract

Background & Objective: Artificial Intelligence (AI) is revitalizing studies in medicine, providing new ways to facilitate learning of either form while raising alarms of its vice compared to virtue. However, there is a scarcity of synthesis of benefits versus limitations of AI in medical studies. Therefore, the objective of this systematic review was to determine the benefits versus drawbacks of AI in medical education with a focus on student engagement, learning outcomes, classroom interaction and cognition.

Methodology: A systematic review was performed in accordance with the PRISMA 2020 reporting guideline. Search was done in Scopus, Web of Science, PubMed, ERIC, and Pakistan search engines (PakMediNet and HEC Digital Library). Studies from 2015 and onwards were included. After screening and eligibility assessment, 26 studies were included. A narrative synthesis of the studies was performed based on heterogeneity.

Results: Some of the benefits of AI in education were better student engagement and participation (18/26) better learning and academic performance (17/26) and better interaction and collaboration (14/26). Major drawbacks were conflicting data on critical thinking (both improvement and overreliance on AI), and the challenges in implementation (especially in developing countries like Pakistan).

Conclusion: AI has the potential to be beneficial to education and provides for interactive, personalized learning. However, cognitive dependency on AI and contextual challenges of implementation also require consideration.