Transferring Domain Knowledge with (X)AI-Based Learning Systems
This addresses the costly and time-consuming issue of expert-led training for novices in high-stakes domains, offering an incremental alternative using XAI.
The paper tackles the problem of training novices in high-stakes domains by using an explainable AI (XAI) system trained on expert decisions to provide examples and explanations, showing in a study with 249 participants that it induces learning and that cognitive styles moderate this effect.
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.