Improving the efficiency of automatic knowledge generation through games and simulations

Abstract

We have created a generalized algorithm for automatically constructing domain level knowledge bases from student input. This method has demonstrated greater efficiencies than when knowledge is hand crafted by subject matter experts (SMEs). This paper presents two related methods for improving automated knowledge acquisition by leveraging the properties of games and simulations. First, we discuss game mechanics that, when added to our intelligent tutor Rashi, lead to higher quantity and quality of student input. In a separate but related analysis, we present a novel game type called a knowledge refinement game (KRG) to improve the knowledge in an expert knowledge base. This game motivates SMEs to refine the generated knowledge base, especially for data in which the system has low confidence. Utilizing an anonymous agreement policy ensures the quality of SME responses and results show that small amounts of KRG activity leads to noticeable improvements in the quality of the knowledge base. We assert that these two results in unison provide evidence that gaming has a powerful potential role in improving artificial intelligence techniques for education. © 2013 Springer-Verlag Berlin Heidelberg.