Domain-Knowledge Manipulation for Dialogue-Adaptive Hinting

1. Introduction Empirical evidence has shown that natural language (NL) dialogue capabilities are a crucial factor to making human explanations effective [6]. Moreover, the use of teaching strategies is an important ingredient for intelligent tutoring systems. Such strategies, normally called dialectic or socratic, have been demonstrated to be superior to pure explanations , especially regarding their long-term effects [8]. Consequently, an increasing though still limited number of state-of-the-art tutoring systems use NL interaction and automatic teaching strategies, including some notion of hints (e.g., [3,7,5]). On the whole, these models of hints are somehow limited in capturing their various underlying functions explicitly and relating them to the domain knowledge dynamically. Our approach is oriented towards integrating hinting in NL dialogue systems [11]. We investigate tutoring proofs in mathematics in a system where domain knowledge, dialogue capabilities, and tutorial phenomena can be clearly identified and intertwined for the automation of tutoring [1]. We aim at modelling a socratic teaching strategy, which allows us to manipulate aspects of learning, such as help the student build a deeper understanding of the domain, eliminate cognitive load, promote schema acquisition, and manipulate motivation levels [13,4,12], within NL dialogue interaction. In contrast to most existing tutorial systems, we make use of a specialised domain reasoner [9]. This design enables detailed reasoning about the student's action and elaborate system feedback [2] Our aim is to dynamically produce hints that fit the needs of the student with regard to the particular proof. Thus, we cannot restrict ourselves to a repertoire of static hints, associating a student answer with a particular response by the system. We developed a multi-dimensional hint taxonomy where each dimension defines a decision point for the associated cognitive function [10]. The domain knowledge can be structured and manipulated for tutoring decision purposes and generation considerations within a tutorial manager. Hint categories abstract from the strict specific domain information and the way it is used in the tutoring, so that it can be replaced for other domains. Thus, the teaching strategy and pedagogical considerations core of the tutorial manager can be retained for different domains. More importantly, the discourse management aspects of the dialogue manager can be independently manipulated.