Transferring Domain Knowledge with (X)AI-Based Learning Systems

TL;DR

This paper explores using explainable AI systems trained on expert decisions to teach novices, demonstrating their effectiveness in inducing learning and highlighting the influence of cognitive styles.

Contribution

It introduces a novel approach of leveraging XAI for human learning, showing its potential as an alternative to traditional expert-guided training.

Findings

XAI-based systems can effectively induce learning in novices

Cognitive styles influence the effectiveness of XAI-based learning

First study to examine XAI's impact on human learning

Abstract

In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However, training novices by experts is costly and time-consuming, increasing the need for alternatives. Explainable artificial intelligence (XAI) has conventionally been used to make black-box artificial intelligence systems interpretable. In this work, we utilize XAI as an alternative: An (X)AI system is trained on experts' past decisions and is then employed to teach novices by providing examples coupled with explanations. In a study with 249 participants, we measure the effectiveness of such an approach for a classification task. We show that (X)AI-based learning systems are able to induce learning in novices and that their cognitive styles moderate learning. Thus, we take the first steps to reveal the impact of XAI on human learning and point AI developers to future options to tailor the design of (X)AI-based learning systems.