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

11 December 2022
Anna Kawakami
Luke M. Guerdan
Yang Cheng
Anita Sun
Alison Hu
Kate Glazko
Nikos Arechiga
Matthew H. Lee
Scott A. Carter
Haiyi Zhu
Kenneth Holstein
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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.

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