From Traditional to AI-Driven Exams: The Evolution of Online Education Assessment

Abstract

This dissertation looks at how traditional assessment methods are changing to AI-driven tests in online education. It discusses the important challenges and chances this change brings for teachers and students. A major focus of this research is how well AI technologies can evaluate student performance compared to old methods. By comparing assessment results, doing surveys to understand how teachers and students feel, and studying AI use in different educational settings, the results show that AI tests can make things faster and offer tailored feedback. However, worries about reliability and bias are still common. In particular, the data shows that teachers and students have different trust levels in AI assessments, which has important effects on teaching practices. In healthcare education, these findings are especially important because precise and accountable assessment methods are crucial. The wider impact of this study hints at a change in how assessments are done, suggesting that while AI technologies can change educational practices, they need to be used carefully while considering ethical issues and differences in how they are applied. Overall, this research adds to the ongoing conversation about educational change, providing insights that could help guide the careful use of AI in assessment systems in healthcare and other areas.