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Towards a Learner-Centered Explainable AI: Lessons from the learning sciences

Authors :
Kawakami, Anna
Guerdan, Luke
Cheng, Yang
Sun, Anita
Hu, Alison
Glazko, Kate
Arechiga, Nikos
Lee, Matthew
Carter, Scott
Zhu, Haiyi
Holstein, Kenneth
Source :
Human-Centered Explainable AI Workshop at ACM CHI Conference on Human Factors in Computing Systems 2022
Publication Year :
2022

Abstract

In this short paper, we argue for a refocusing of XAI around human learning goals. Drawing upon approaches and theories from the learning sciences, we propose a framework for the learner-centered design and evaluation of XAI systems. We illustrate our framework through an ongoing case study in the context of AI-augmented social work.<br />Comment: 7 pages, 2 figures

Details

Database :
arXiv
Journal :
Human-Centered Explainable AI Workshop at ACM CHI Conference on Human Factors in Computing Systems 2022
Publication Type :
Report
Accession number :
edsarx.2212.05588
Document Type :
Working Paper