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arxiv: 2212.05588 · v1 · pith:PWG5DX2Cnew · submitted 2022-12-11 · 💻 cs.HC

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

Anna Kawakami , Luke Guerdan , Yang Cheng , Anita Sun , Alison Hu , Kate Glazko , Nikos Arechiga , Matthew Lee
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Scott Carter Haiyi Zhu Kenneth Holstein
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classification 💻 cs.HC
keywords learningframeworklearner-centeredsciencesai-augmentedapproachesarguearound
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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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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

    cs.AI 2026-04 unverdicted novelty 4.0

    Infusing learning theories into the XAI lifecycle offers a learner-centered path to improve human agency and mitigate explanation-related risks in AI systems.