Nursing students using the GoodNurse AI model achieved significantly higher grades in an ECG course compared to nonusers (95.1% vs 88.8%; P=0.0048).
Observational
Does the use of domain-specific AI (GoodNurse) improve student performance and diagnostic accuracy in nursing ECG education?
Domain-specific generative AI enhances ECG learning, diagnostic accuracy, and cost-efficiency in nursing education.
Absolute Event Rate: 95.1% vs 88.8%
p-value: p=0.0048
Background: Electrocardiogram (ECG) interpretation is a critical yet challenging skill for nurses. Generative artificial intelligence (AI) offers potential for personalized, adaptive learning. Purpose: The aim was to evaluate the effectiveness, accuracy, and cost-efficiency of AI models in nursing ECG education. Methods: A 2-part study compared 4 AI models (GoodNurse, ChatGPT-5, Claude Sonnet 4, Microsoft Copilot) on an 88-item ECG exam and assessed cost-effectiveness. GoodNurse was then integrated into a 4-credit ECG course; AI usage, satisfaction, and grades were analyzed. Results: Accuracy varied (P <. 01): GoodNurse 85. 3%, ChatGPT-5 83. 1%, Copilot 80. 9%, Sonnet 79. 1%. GoodNurse had the fewest waveform errors and the best cost-per-accuracy (8. 06 per 1% gain). In the course, 43% of students used GoodNurse, achieving higher grades (95. 1 ± 2. 5%) than nonusers (88. 8 ± 5. 9%; P =. 0048), with a 5. 80 per 1% grade improvement. Conclusion: Domain-specific AI, such as GoodNurse, enhances ECG learning, diagnostic accuracy, and cost-efficiency, supporting its integration into nursing education.
Dzikowicz et al. (Thu,) conducted a observational in ECG interpretation education. GoodNurse AI model vs. Nonusers (students) and other AI models (ChatGPT-5, Claude Sonnet 4, Microsoft Copilot) was evaluated on Student grades in a 4-credit ECG course (p=0.0048). Nursing students using the GoodNurse AI model achieved significantly higher grades in an ECG course compared to nonusers (95.1% vs 88.8%; P=0.0048).