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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
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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.

Peer review status:IN REVISION

16 Apr 2021Submitted to Applied AI Letters
19 Apr 2021Assigned to Editor
19 Apr 2021Submission Checks Completed
08 Jun 2021Reviewer(s) Assigned
02 Jul 2021Review(s) Completed, Editorial Evaluation Pending
05 Jul 2021Editorial Decision: Revise Minor