A paper recently accepted for publication and an upcoming panel discussion both focus on the topic of personalized education using machine learning and AI tools—one of the van der Schaar Lab’s ongoing areas of research.
Just as the lab’s approach to healthcare is premised on the fact that all patients are different, our approach to education reflects the incredible heterogeneity between students in terms of abilities, backgrounds, interests, goals, needs and priorities. Since a one-size-fits-all model for education will seldom yield optimal results for any given student, we are particularly interested in the application of machine learning and AI tools to deliver truly personalized education.
This is the focus of a recent paper co-authored by Mihaela van der Schaar, entitled “Personalized Education in the AI Era: What to Expect Next?” In this paper, Mihaela and co-authors Setareh Maghsudi, Andrew Lan, and Jie Xu provide a brief review of state-of-the-art research, investigate the challenges of machine learning/AI-based personalized education, and discuss potential solutions. The paper was recently accepted for publication in the IEEE Signal Processing Magazine (editor-in-chief: Christian Jutten).
Personalized Education in the AI Era: What to Expect Next?
Setareh Maghsudi, Andrew Lan, Jie Xu, Mihaela van der Schaar
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner’s strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student’s characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners’ features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like.
Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like.
In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.
Mihaela will also discuss the latest developments related to AI and machine learning tools for personalized education at a virtual panel hosted by ODSC, which will take place on February 23.
To see our previous work in the area of personalized education, click here.