The Revolution in Midwifery Education: How AI and Deep Learning are Transforming Outcome-Based Assessments?

BACKGROUND

Currently, midwifery education is confronted with a variety of obstacles, such as inadequate resources and conventional learning methods that are less effective in enhancing the clinical skills of students. Technological advancements and the rapid evolution of maternal and neonatal health services necessitate the transformation of midwifery education to a competency-based curriculum and outcome-based assessment paradigm. Artificial intelligence (AI) and deep learning have the potential to provide adaptive, personalized, and precise learning in this context. Nevertheless, its implementation continues to encounter a variety of challenges.

PURPOSE

This study reviews the role of AI and deep learning algorithms in enhancing outcome-based assessments in midwifery education, focusing on improvements in objectivity, personalized learning, and students' clinical readiness.

PATIENTS AND METHODS

This study employed a systematic literature review from Science Direct, Semantic Scholar, Springer Nature, and Taylor and Francis databases. Rayyan's software was employed to select 15 articles from the 771 articles that were discovered, in accordance with the inclusion and exclusion criteria. To guarantee objectivity and quality, two researchers conducted an independent evaluation.

RESULTS

Our review indicates that algorithms including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM) are proficient in facilitating objective evaluations, delivering tailored feedback, and enhancing clinical learning simulations. Artificial intelligence has demonstrated the capacity to enhance students' communication, critical thinking, and clinical decision-making abilities. The primary challenges encompass infrastructure preparedness, digital literacy, and ethical concerns pertaining to data protection and algorithmic prejudice.

CONCLUSION

Artificial intelligence and deep learning possess significant promise to revolutionize achievement-based assessments in midwifery education through accurate, adaptable, and scalable evaluations. The successful implementation relies on the management of technological, pedagogical, and ethical restrictions, along with thorough integration into the curriculum to equip graduates for global maternal and neonatal health concerns.