An Interaction Design for Machine Teaching to Develop AI Tutors

要旨

Intelligent tutoring systems (ITSs) have consistently been shown to improve the educational outcomes of students when used alone or combined with traditional instruction. However, building an ITS is a time-consuming process which requires specialized knowledge of existing tools. Extant authoring methods, including the Cognitive Tutor Authoring Tools' (CTAT) example-tracing method and SimStudent's Authoring by Tutoring, use programming-by-demonstration to allow authors to build ITSs more quickly than they could by hand programming with model-tracing. Yet these methods still suffer from long authoring times or difficulty creating complete models. In this study, we demonstrate that Simulated Learners built with the Apprentice Learner (AL) Framework can be combined with a novel interaction design that emphasizes model transparency, input flexibility, and problem solving control to enable authors to achieve greater model completeness in less time than existing authoring methods.

キーワード
{Simulated Learners
Interaction Design
Programming-by-Demonstration
Machine Teaching
Intelligent Tutoring Systems
著者
Daniel Weitekamp
Carnegie Mellon University, Pittsburgh, PA, USA
Erik Harpstead
Carnegie Mellon University, Pittsburgh, PA, USA
Ken R. Koedinger
Carnegie Mellon University, Pittsburgh, PA, USA
DOI

10.1145/3313831.3376226

論文URL

https://doi.org/10.1145/3313831.3376226

動画

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Tutoring & learning

Paper session
313A O'AHU
5 件の発表
2020-04-27 20:00:00
2020-04-27 21:15:00
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