  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.
