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An Interaction Design for Machine Teaching to Develop AI Tutors

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Title: An Interaction Design for Machine Teaching to Develop AI Tutors
Language: English
Authors: Daniel Weitekamp III (ORCID 0000-0003-0079-8000), Erik Harpstead (ORCID 0000-0003-3019-3627), Kenneth R. Koedinger (ORCID 0000-0002-5850-4768)
Source: Grantee Submission. 2020.
Peer Reviewed: Y
Page Count: 11
Publication Date: 2020
Sponsoring Agency: Institute of Education Sciences (ED)
Google LLC
Contract Number: R305B150008
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Instructional Design, Simulation, Educational Technology, Graduate Students, Doctoral Students, Prior Learning, Skill Development, Problem Solving
DOI: 10.1145/3313831.3376226
Abstract: 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. [This paper was published in: "CHI '20 Conference on Human Factors in Computing Systems," ACM, 2020, Paper 99.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED663759
Database: ERIC
FullText Text:
  Availability: 0
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  Data: An Interaction Design for Machine Teaching to Develop AI Tutors
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  Data: <searchLink fieldCode="AR" term="%22Daniel+Weitekamp+III%22">Daniel Weitekamp III</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0079-8000">0000-0003-0079-8000</externalLink>)<br /><searchLink fieldCode="AR" term="%22Erik+Harpstead%22">Erik Harpstead</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3019-3627">0000-0003-3019-3627</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kenneth+R%2E+Koedinger%22">Kenneth R. Koedinger</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5850-4768">0000-0002-5850-4768</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2020.
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  Data: 11
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  Data: 2020
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  Data: Institute of Education Sciences (ED)<br />Google LLC
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  Data: 10.1145/3313831.3376226
– Name: Abstract
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  Data: 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. [This paper was published in: "CHI '20 Conference on Human Factors in Computing Systems," ACM, 2020, Paper 99.]
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  Data: 2025
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        Value: 10.1145/3313831.3376226
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
    Subjects:
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Intelligent Tutoring Systems
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      – SubjectFull: Problem Solving
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      – TitleFull: An Interaction Design for Machine Teaching to Develop AI Tutors
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