Learning by Experiencing: An Immersive Digital Twin Tool for ECG Education

Abstract

Practical training in electrocardiogram (ECG) interpretation remains uneven, particularly in resource-limited settings, despite the central role of ECGs in cardiovascular diagnosis. This work evaluates whether an ECG-focused digital twin that integrates interactive simulation and deep learning guidance can achieve educationally valid realism, improve recognition of patterns and abnormalities through interactivity, and enhance accuracy and learner motivation via predictive feedback. We present ECGTwinMentor, a cross-platform system that synthesizes parameterized ECG waveforms, enables fine-grained control of physiologic variables, and delivers immediate predictive feedback for formative assessment. The diagnostic model supports low-latency inference on modest hardware. Validation with healthcare experts and medical students showed positive evaluations for realism, usability, and integration potential. Experts reported average ratings between 3.5 and 4.5 out of 5, while students rated usability between 4.6 and 4.8 and motivation and realism at 5.0, with most items scoring at least 4. These findings support the conclusion that an interactive, predictive digital twin can narrow the gap between theory and practice in ECG interpretation, offering an accessible, scalable, and reproducible approach to ECG education.