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

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

URL: http://arxiv.org/abs/2406.01329v1
Date: Mon, 3 Jun 2024 13:56:30 GMT
Title: Transferring Domain Knowledge with (X)AI-Based Learning Systems
Authors: Philipp Spitzer, Niklas Kühl, Marc Goutier, Manuel Kaschura, Gerhard Satzger,
Abstract summary: Explainable artificial intelligence (XAI) has conventionally been used to make black-box artificial intelligence systems interpretable. An (X)AI system is trained on experts' past decisions and is then employed to teach novices by providing examples and explanations. We show that (X)AI-based learning systems are able to induce learning in novices and that their cognitive styles moderate learning.
Score: 3.0059120458540383
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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.

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