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.
@article{arxiv.2212.05588,
title = {Towards a Learner-Centered Explainable AI: Lessons from the learning sciences},
author = {Anna Kawakami and Luke Guerdan and Yang Cheng and Anita Sun and Alison Hu and Kate Glazko and Nikos Arechiga and Matthew Lee and Scott Carter and Haiyi Zhu and Kenneth Holstein},
journal= {arXiv preprint arXiv:2212.05588},
year = {2022}
}