# EduQate: Generating Adaptive Curricula through RMABs in Education Settings

arXiv: https://arxiv.org/abs/2406.14122 · HTML: https://mlsanity.com/paper/2406.14122/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Collapsing Bandits and Their Application to Public Health Interventions](https://mlsanity.com/paper/2007.04432/) (2020)
- [Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare](https://mlsanity.com/paper/2105.07965/) (2021)
- [Deep Reinforcement Learning of Marked Temporal Point Processes](https://mlsanity.com/paper/1805.09360/) (2018)
- [Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content](https://mlsanity.com/paper/1804.05212/) (2018)
- [Networked Restless Bandits with Positive Externalities](https://mlsanity.com/paper/2212.05144/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild](https://mlsanity.com/paper/2606.11744/) (2026)

## Abstract (arXiv)

There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in developing such tools is in exploring how mastery can be achieved across a diverse yet related range of content in an efficient manner. While Reinforcement Learning and Multi-armed Bandits have shown promise in educational settings, existing works often assume the independence of learning content, neglecting the prevalent interdependencies between such content. In response, we introduce Education Network Restless Multi-armed Bandits (EdNetRMABs), utilizing a network to represent the relationships between interdependent arms. Subsequently, we propose EduQate, a method employing interdependency-aware Q-learning to make informed decisions on arm selection at each time step. We establish the optimality guarantee of EduQate and demonstrate its efficacy compared to baseline policies, using students modeled from both synthetic and real-world data.