Improving Artificial Teachers by Considering How People Learn and Forget

Aurélien Nioche*, Pierre-Alexandre Murena, Carlos de la Torre Ortiz, Antti Oulasvirta

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

35 Downloads (Pure)


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).
Original languageEnglish
Title of host publication26th International Conference on Intelligent User Interfaces, IUI 2021
Number of pages9
ISBN (Electronic)9781450380171
Publication statusPublished - 14 Apr 2021
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Intelligent User Interfaces - Virtual, Online, College Station, United States
Duration: 13 Apr 202117 Apr 2021
Conference number: 26


ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
Country/TerritoryUnited States
CityCollege Station


Dive into the research topics of 'Improving Artificial Teachers by Considering How People Learn and Forget'. Together they form a unique fingerprint.

Cite this