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

**Authors:** Setareh Maghsudi, Andrew Lan, Jie Xu, Mihaela van der Schaar  
**arXiv:** [2101.10074](https://arxiv.org/abs/2101.10074)  
**Date:** 2021-01-19  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2101.10074v1/

## Key points

- The objective of personalized learning is to match the learner's strengths and bypass weaknesses to meet desired goals.
- Personalized learning is being adopted by a growing number of educational institutions worldwide.
- Artificial intelligence (AI) and machine learning (ML) have enhanced personalized education through big data analysis.
- AI/ML methods help acquire student characteristics, recommend appropriate content, advise curriculum, connect learners, and provide performance evaluation.
- Challenges in AI-based personalized education include compensating for peer absence effects, maintaining motivation, increasing diversity in materials and approaches, and removing biases from data and algorithms.
- The paper titled "Personalized Education in the AI Era: What to Expect Next?" reviews state-of-the-art research on AI/ML-based personalized education and discusses associated challenges and potential solutions.
- AI/ML technologies can revolutionize personalized education by tailoring learning experiences based on individual strengths and weaknesses.

## Summary

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 by observing their past experiences and analyzing available big data to explore their features and similarities. The platform can recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners through suggestions, provide accurate performance evaluation, and more. However, there are still several aspects of AI-based personalized education that remain unexplored. These include compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity in educational materials and approaches, removing biases induced by data and algorithms, among others. In this paper titled "Personalized Education in the AI Era: What to Expect Next? ", authors Setareh Maghsudi, Andrew Lan, Jie Xu, and Mihaela van der Schaar provide a brief review of state-of-the-art research on AI/ML-based personalized education. They also investigate the challenges associated with it and discuss potential solutions. Overall, this paper highlights how AI/ML technologies can revolutionize personalized education by leveraging big data analysis to tailor learning experiences according to individual strengths and weaknesses. It also emphasizes the need for further exploration into addressing challenges such as peer absence effects compensation; motivation maintenance; diversity enhancement; bias removal from data and algorithms; in order to fully unlock the potential of AI-based personalized education.