A Comprehensive Review of Reinforcement Learning in Intelligent Allocation and Optimization of Educational Resources

Authors

  • Zhonglin Zhao Meicheng East, LLC

DOI:

https://doi.org/10.70393/6a6374616d.323436

ARK:

https://n2t.net/ark:/40704/JETBM.v1n6a03

Disciplines:

Artificial Intelligence and Intelligence

Subjects:

Machine Learning

References:

20

Keywords:

Reinforcement Learning, Educational Resource Optimization, Equity in Education, Multi-Agent Systems, Personalized Learning, Resource Scheduling, Hybrid AI Approaches, Educational Data Quality

Abstract

Educational resource imbalances pose considerable barriers to accomplishing equitable opportunities to learn worldwide. Traditional approaches to resource allocation frequently fail to adapt to the ever-changing and intricate needs of educational systems, exacerbating disparities. This paper explores applying reinforcement learning (RL) in optimizing how resources in education are distributed and used, offering a promising solution to these hurdles. The analysis delves into fundamental RL concepts and algorithms, like deep reinforcement learning and multi-agent reinforcement learning, and investigates their applications in customized learning, scheduling resources, and promoting fairness. It highlights major difficulties such as information quality, scalability, fairness, and transparency, along with possibilities for innovation through blended methodologies and instant decision making. By combining existing research and distinguishing critical gaps, this study provides practical insights for advancing RL applications in education, paving the way for more inclusive and effective systems for managing resources.

Author Biography

Zhonglin Zhao, Meicheng East, LLC

Meicheng East, LLC, USA.

References

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Published

2024-12-16

How to Cite

Zhao, Z. (2024). A Comprehensive Review of Reinforcement Learning in Intelligent Allocation and Optimization of Educational Resources. Journal of Economic Theory and Business Management, 1(6), 15–24. https://doi.org/10.70393/6a6374616d.323436

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