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

arXiv: https://arxiv.org/abs/2212.05588 · HTML: https://mlsanity.com/paper/2212.05588/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2022.

## Built on by (most cited first, 3 of 3)

- [Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools](https://mlsanity.com/paper/2303.00192/) (2023)
- [Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making](https://mlsanity.com/paper/2305.16700/) (2023)
- [Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges](https://mlsanity.com/paper/2604.19788/) (2026)

## 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.