Personalized Education in the AI Era: What to Expect Next?

TL;DR

This paper reviews how AI and machine learning are transforming personalized education by enabling tailored learning experiences, while also discussing unresolved challenges and potential solutions for future development.

Contribution

It provides a comprehensive review of current AI-driven personalized education methods and discusses key challenges and possible solutions for advancing the field.

Findings

AI/ML enhances personalized learning through data analysis and tailored content.

Several challenges remain, including motivation, diversity, and bias mitigation.

The paper suggests potential solutions to improve AI-based educational personalization.

Abstract

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.