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

arXiv: https://arxiv.org/abs/2101.10074 · HTML: https://mlsanity.com/paper/2101.10074/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [Discourse Level Factors for Sentence Deletion in Text Simplification](https://mlsanity.com/paper/1911.10384/) (2019)
- [TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts](https://mlsanity.com/paper/1902.06034/) (2019)
- [Personalized Course Sequence Recommendations](https://mlsanity.com/paper/1512.09176/) (2015)
- [Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?](https://mlsanity.com/paper/1812.03557/) (2018)
- [Incentive Design in Peer Review: Rating and Repeated Endogenous Matching](https://mlsanity.com/paper/1411.2139/) (2014)
- [Personalized Thread Recommendation for MOOC Discussion Forums](https://mlsanity.com/paper/1806.08468/) (2018)
- [Computational Models of Human Decision-Making with Application to the Internet of Everything](https://mlsanity.com/paper/2008.11958/) (2020)
- [eTutor: Online Learning for Personalized Education](https://mlsanity.com/paper/1410.3617/) (2014)

## Built on by (most cited first, 13 of 13)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [The Key Artificial Intelligence Technologies in Early Childhood Education: A Review](https://mlsanity.com/paper/2401.05403/) (2023)
- [Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning](https://mlsanity.com/paper/2308.10454/) (2023)
- [Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration](https://mlsanity.com/paper/2407.16887/) (2024)
- [Human-Centric eXplainable AI in Education](https://mlsanity.com/paper/2410.19822/) (2024)
- [Findings of MEGA: Maths Explanation with LLMs using the Socratic Method for Active Learning](https://mlsanity.com/paper/2507.12079/) (2025)
- [Personalized Learning Path Planning with Goal-Driven Learner State Modeling](https://mlsanity.com/paper/2510.13215/) (2025)
- [Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education](https://mlsanity.com/paper/2509.23996/) (2025)
- [MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning](https://mlsanity.com/paper/2304.04668/) (2023)
- [From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions](https://mlsanity.com/paper/2308.15225/) (2023)
- [Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts](https://mlsanity.com/paper/2403.03920/) (2024)
- [Integrating AI in College Education: Positive yet Mixed Experiences with ChatGPT](https://mlsanity.com/paper/2407.05810/) (2024)
- [SocialCoach: Personalized Social Skill Learning with Agentic Tutoring and Practice](https://mlsanity.com/paper/2606.04155/) (2026)

## Abstract (arXiv)

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.