Towards a Learner-Centered Explainable AI: Lessons from the learning sciences
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 11 Dec 2022.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2023Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Toolscited by 16
- 2023Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Makingcited by 1
- 2026Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challengescited by 0
- 2026Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges1 Apr 2026
- 2023Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making26 May 2023
- 2023Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools1 Mar 2023