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Methods and Standards for Research on Explainable Artificial Intelligence: Lessons from Intelligent Tutoring Systems
  • Robert Hoffman,
  • William Clancey
Robert Hoffman
Florida Institute for Human and Machine Cognition

Corresponding Author:[email protected]

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William Clancey
Florida Institute for Human and Machine Cognition
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Abstract

We reflect on the progress in the area of Explainable AI (XAI) Program relative to previous work in the area of intelligent tutoring systems (ITS). A great deal was learned about explanation—and many challenges uncovered—in research that is directly relevant to XAI. We suggest opportunities for future XAI research deriving from ITS methods, as well as the challenges shared by both ITS and XAI in using AI to assist people in solving difficult problems effectively and efficiently.
16 Apr 2021Submitted to Applied AI Letters
19 Apr 2021Submission Checks Completed
19 Apr 2021Assigned to Editor
08 Jun 2021Reviewer(s) Assigned
02 Jul 2021Review(s) Completed, Editorial Evaluation Pending
05 Jul 2021Editorial Decision: Revise Minor
04 Aug 20211st Revision Received
05 Aug 2021Submission Checks Completed
05 Aug 2021Assigned to Editor
18 Aug 2021Reviewer(s) Assigned
19 Aug 2021Review(s) Completed, Editorial Evaluation Pending
31 Aug 2021Editorial Decision: Revise Minor
10 Sep 20212nd Revision Received
11 Sep 2021Submission Checks Completed
11 Sep 2021Assigned to Editor
04 Oct 2021Reviewer(s) Assigned
22 Oct 2021Review(s) Completed, Editorial Evaluation Pending
25 Oct 2021Editorial Decision: Accept