View in EDS

Reinforcement Learning in Education: A Systematic Literature Review.

Saved in:
Bibliographic Details
Title: Reinforcement Learning in Education: A Systematic Literature Review.
Authors: Riedmann, Anna1 (AUTHOR) anna.riedmann@uni-wuerzburg.de, Schaper, Philipp1 (AUTHOR), Lugrin, Birgit1 (AUTHOR)
Source: International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.). Dec2025, Vol. 35 Issue 5, p2669-2723. 55p.
Subject Terms: *Machine learning, *Student engagement, *Science education, *Educational outcomes, *Best practices, *Educational intervention, Reinforcement learning
Abstract: We conducted a systematic literature review to examine the current research on the application of Reinforcement Learning (RL) in education. RL is a type of Machine Learning that trains an agent to take actions in an environment in order to maximize a reward signal. In recent years, researchers have explored the potential of RL for improving educational outcomes and developing personalized interventions. This systematic review (according to the PRISMA standard) surveys and evaluates 89 manuscripts from three databases (IEEE Xplore, Google Scholar, and ACM) published between 2000 and 2024 with predefined eligibility criteria. We examined the following objectives: (1) Educational contexts and evaluation strategies in RL-based educational applications, (2) impact and significance of RL-based applications for cognitive and affective variables, (3) RL algorithms and baselines used in the context of RL in education, (4) adaptation mechanisms in RL-based education, and (5) best practices for implementing RL in education. Our results suggest that RL has shown promise for a range of educational applications, such as enhancing learning outcomes or promoting student engagement. However, there are currently significant challenges and limitations to the use of RL in education, including methodological issues, and the need for broader and more large-scale deployments and evaluations with actual users relative to only using simulated data. Overall, this review provides a comprehensive overview of the current state of research on the application of RL in education and identifies areas where further research is needed to fully realize its potential as a tool for enhancing teaching and learning. Additionally, we present a set of best practices for the field, distilling key insights from our systematic review for practical application. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
Description
Abstract:We conducted a systematic literature review to examine the current research on the application of Reinforcement Learning (RL) in education. RL is a type of Machine Learning that trains an agent to take actions in an environment in order to maximize a reward signal. In recent years, researchers have explored the potential of RL for improving educational outcomes and developing personalized interventions. This systematic review (according to the PRISMA standard) surveys and evaluates 89 manuscripts from three databases (IEEE Xplore, Google Scholar, and ACM) published between 2000 and 2024 with predefined eligibility criteria. We examined the following objectives: (1) Educational contexts and evaluation strategies in RL-based educational applications, (2) impact and significance of RL-based applications for cognitive and affective variables, (3) RL algorithms and baselines used in the context of RL in education, (4) adaptation mechanisms in RL-based education, and (5) best practices for implementing RL in education. Our results suggest that RL has shown promise for a range of educational applications, such as enhancing learning outcomes or promoting student engagement. However, there are currently significant challenges and limitations to the use of RL in education, including methodological issues, and the need for broader and more large-scale deployments and evaluations with actual users relative to only using simulated data. Overall, this review provides a comprehensive overview of the current state of research on the application of RL in education and identifies areas where further research is needed to fully realize its potential as a tool for enhancing teaching and learning. Additionally, we present a set of best practices for the field, distilling key insights from our systematic review for practical application. [ABSTRACT FROM AUTHOR]
ISSN:15604292
DOI:10.1007/s40593-025-00494-6