DOI: https://doi.org/10.36347/sjmcr.2026.v14i06.053

Pages: 1543-1550

Background: Electrocardiogram (ECG) interpretation is a fundamental clinical competency expected from graduating medical students. Despite its importance in diagnosing arrhythmias, acute coronary syndromes, and conduction abnormalities, ECG interpretation remains a major challenge in undergraduate medical education. Traditional lecture-based instruction often fails to ensure long-term competency and confidence among medical students. Objective: This narrative review aims to summarize current evidence regarding ECG teaching strategies for medical students, identify major educational barriers, and discuss future directions in undergraduate ECG education. Materials and Methods: A narrative review of the literature was conducted using PubMed, Scopus, Google Scholar, and Web of Science databases. Articles published in English between 2000 and 2025 were screened using combinations of the keywords “electrocardiogram,” “ECG,” “medical students,” “medical education,” “simulation,” “e-learning,” “artificial intelligence,” and “cardiology education.” Original studies, systematic reviews, educational interventions, and expert recommendations related to undergraduate ECG education were included. Results: Contemporary evidence suggests that active learning approaches outperform traditional didactic teaching alone. Educational methods associated with improved ECG competency include case-based learning, flipped classrooms, simulation-based training, peer-assisted teaching, spaced repetition, and digital learning platforms [1-12]. E-learning and mobile applications provide flexible opportunities for repetitive practice and self-directed learning [5-7]. Simulation-based education improves clinical reasoning, confidence, and emergency preparedness [8,9]. Artificial intelligence-assisted educational tools may further personalize ECG learning and provide adaptive feedback [24-27]. However, several barriers persist, including curriculum fragmentation, cognitive overload, inadequate clini