  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.
