Ever since MYCIN introduced the idea of computer-based explanations to the artificial intelligence community, it has come to be taken for granted that all knowledge-based systems (KBS) need to provide explanations. While this widely-held belief has led to much research on the generation and implementation of various kinds of explanations, there has been no theoretical basis to justify the use of explanations by KBS users. This paper discusses the role of KBS explanations to provide an understanding of both the specific factors that influence explanation use and the consequences of such use. The first part of the paper proposes a model based on cognitive learning theories to identify the reasons for the provision of KBS explanations from the perspective of facilitating user learning. Using the feedforward and feedback operators of cognitive learning the paper develops strategies for providing KBS explanations and classifies the various types of explanations found in current KBS applications. This second part of the paper presents a two-part framework to investigate empirically the use of KBS explanations. The first part of the framework focuses on the potential factors that influence the explanation seeking behavior of KBS users, including user expertise, the types of explanations provided and the level of user agreement with the KBS. The second part of the framework explores the potential effects of the use of KBS explanations and specifically considers four distinct categories of potential effects: explanation use behavior, learning, perceptions, and judgmental decision making.