The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses his/her weaknesses to ultimately meet his/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 rise of artificial intelligence (AI) and machine learning (ML), together with advances in big data analysis, has introduced novel perspectives that enhance personalized education in numerous ways. By taking advantage of AI/ML methods, the educational platform precisely acquires the student?s characteristics. This is done, in part, by observing 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, and connect appropriate learners by suggestion, accurate performance evaluation, and so forth. 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 the diversity, removing the biases induced by data and algorithms, and so on. In this article, 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.

中文翻译：

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人工智能时代的个性化教育：未来展望

个性化学习的目的是设计一条有效的知识获取路径，使其与学习者的长处相匹配，并绕过他/她的弱点，最终实现他/她的预期目标。这个概念是在几年前出现的，并被全球范围内迅速增长的教育机构所采用。近年来，人工智能（AI）和机器学习（ML）的兴起，以及大数据分析的进步，带来了新颖的观点，这些观点以多种方式增强了个性化教育。通过利用AI / ML方法，该教育平台可以准确地获取学生的特征。这部分是通过观察过去的经验以及通过探索学习者的特征和相似性来分析可用的大数据来完成的。例如，它可以 在众多可访问的内容中推荐最合适的内容，为精心设计的长期课程提供建议，并通过建议，准确的绩效评估等联系合适的学习者。尽管如此，基于AI的个性化教育的几个方面仍待探索。这些措施尤其包括补偿因缺少同伴而产生的不利影响，建立和维护学习动机，增加多样性，消除由数据和算法引起的偏见等。在本文中，我们在简要回顾最新研究的同时，探讨了基于AI / ML的个性化教育所面临的挑战，并讨论了潜在的解决方案。准确的性能评估等。尽管如此，基于AI的个性化教育的几个方面仍待探索。这些措施尤其包括补偿因缺少同伴而产生的不利影响，建立和维护学习动机，增加多样性，消除由数据和算法引起的偏见等。在本文中，我们在简要回顾最新研究的同时，探讨了基于AI / ML的个性化教育所面临的挑战，并讨论了潜在的解决方案。准确的性能评估等。尽管如此，基于AI的个性化教育的几个方面仍待探索。这些措施尤其包括补偿因缺少同伴而产生的不利影响，建立和维护学习动机，增加多样性，消除由数据和算法引起的偏见等。在本文中，我们在简要回顾最新研究的同时，探讨了基于AI / ML的个性化教育所面临的挑战，并讨论了潜在的解决方案。消除由数据和算法等引起的偏差。在本文中，我们在简要回顾最新研究的同时，探讨了基于AI / ML的个性化教育所面临的挑战，并讨论了潜在的解决方案。消除由数据和算法等引起的偏差。在本文中，我们在简要回顾最新研究的同时，探讨了基于AI / ML的个性化教育所面临的挑战，并讨论了潜在的解决方案。