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Towards a Learner-Centered Explainable AI: Lessons from the learning sciences
December 11, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Anna Kawakami, Luke Guerdan, Yang Cheng, Anita Sun, Alison Hu, Kate Glazko, Nikos Arechiga, Matthew Lee, Scott Carter, Haiyi Zhu, Kenneth Holstein
arXiv ID
2212.05588
Category
cs.HC: Human-Computer Interaction
Citations
10
Venue
arXiv.org
Last Checked
4 months ago
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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