This paper describes design philosophy of a knowledge acquisition system named KAISER (a Knowledge Acquisition Inductive System driven by Explanatory Reasoning). This system is an intelligent workbench for constructing knowledge bases for classification tasks. As the acronym implies, KAISER first learns classification knowledge inductively from examples given by human experts, then analyzes the result based on abstract domain knowledge which is also given by the experts, in order to detect some unsatisfactory and/or unreliable conditions called improprieties. By interpreting the improprieties, this system invokes intelligent interview for acquiring new knowledge to eliminate the improprieties. The interview stimulates the human experts and help them to revise the learned results, control the learning process and remind new examples and domain knowledge. Viewed in AI aspect, this process provides reasonable motivation for interview by integrating similarity-based inductive learning and explanation based deductive reasoning based on the concept of improprieties. This approach makes it possible to compensate for the problems of inductive learning, explanation and knowledge acquisition interview each other. A simple example of diagnosis problem is also provided to clarify and evaluate the knowledge acquisition process.