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

TL;DR

This paper advocates for a shift in explainable AI towards aligning with human learning goals, proposing a learner-centered framework inspired by learning sciences, demonstrated through a social work case study.

Contribution

It introduces a novel learner-centered framework for XAI design and evaluation based on learning sciences principles.

Findings

Framework aligns XAI with human learning objectives

Case study demonstrates practical application in social work

Highlights importance of educational principles in XAI design

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.