# Improving Artificial Teachers by Considering How People Learn and Forget

arXiv: https://arxiv.org/abs/2102.04174 · HTML: https://mlsanity.com/paper/2102.04174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Multi-Armed Bandits for Intelligent Tutoring Systems](https://mlsanity.com/paper/1310.3174/) (2013)
- [Teaching Multiple Concepts to a Forgetful Learner](https://mlsanity.com/paper/1805.08322/) (2018)
- [Unbounded Human Learning: Optimal Scheduling for Spaced Repetition](https://mlsanity.com/paper/1602.07032/) (2016)

## Built on by (most cited first, 4 of 4)

- [Storyfier: Exploring Vocabulary Learning Support with Text Generation Models](https://mlsanity.com/paper/2308.03864/) (2023)
- [Online simulator-based experimental design for cognitive model selection](https://mlsanity.com/paper/2303.02227/) (2023)
- [Least Squares Maximum and Weighted Generalization-Memorization Machines](https://mlsanity.com/paper/2308.16456/) (2023)
- [Inducing Individual Students' Learning Strategies through Homomorphic POMDPs](https://mlsanity.com/paper/2403.10930/) (2024)

## Abstract (arXiv)

The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Whereas previous work has focused on either personalization of teaching or optimization of teaching intervention sequences, the proposed individualized model-based planning approach represents convergence of these two lines of research. Model-based planning picks the best interventions via interactive learning of a user memory model's parameters. The approach is novel in its use of a cognitive model that can account for several key individual- and material-specific characteristics related to recall/forgetting, along with a planning technique that considers users' practice schedules. Taking a rule-based approach as a baseline, the authors evaluated the method's benefits in a controlled study of artificial teaching in second-language vocabulary learning (N=53).