Multi-armed Bandit Algorithms for Adaptive Learning: A Survey

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

6 Citations (Scopus)


Adaptive learning aims to provide each student individual tasks specifically tailed to his/her strengths and weaknesses. However, it is challenging to realize it, overcoming the complexity issue in online learning. There are many unsolved problems such as knowledge component sequencing, activity sequencing, exercise sequencing, question sequencing, and pedagogical strategy, to realize adaptive learning. Bandit algorithms are particularly suitable to model the process of planning and using feedback on the outcome of that decision to inform future decisions. They are finding their way into practical applications in various areas especially in online platforms where data is readily available, and automation is the only way to scale. This paper presents a survey on bandit algorithms for facilitating adaptive learning in different settings. The findings indicate that the various bandit algorithms have great potential to solve the above problems. Also, we discuss issues and challenges of developing and using adaptive learning systems based on the multi-armed bandit framework.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
EditorsIdo Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin, Vania Dimitrova
Number of pages6
Publication statusPublished - 2021
Event22nd International Conference on Artificial Intelligence in Education, AIED 2021 - Virtual, Online
Duration: 14 Jun. 202118 Jun. 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12749 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Artificial Intelligence in Education, AIED 2021
CityVirtual, Online


  • Adaptive learning
  • Bandit algorithms
  • Exploration and exploitation
  • Multi-armed bandit algorithm
  • Personalized learning


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