Traditional machine learning models rely on closed-world assumptions, restricting their ability to adapt to dynamic and evolving environments, such as online learning platforms. This paper investigates the use of Open-World Machine Learning (OWML) to detect and classify previously unseen student engagement behaviors in online education. We propose a robust framework that integrates outlier detection and incremental learning, leveraging an open-source dataset provided by EdNet, to dynamically identify and adapt to emerging engagement patterns. Experimental results demonstrate a significant improvement in detecting novel behaviors compared to baseline models, highlighting OWML’s potential to enhance adaptive learning in online education. Additionally, we address ethical considerations, including data privacy and fairness in behavior classification, to promote equitable and transparent adaptive learning interventions.