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A probabilistic conversational agent for intelligent tutoring systems

The paper proposes a novel approach for a Conversational Intelligent Tutoring System, which combines Natural Language Processing techniques with dynamic Bayesian Inference for probabilistic student state estimation. Our aim is to develop and implement the probabilistic model for the discourse between the user and the Conversational Agent. Most of the already proposed tutoring systems interacting with the user via natural language use rather simple pattern matching rules to carry on the conversation. Even when the fuzzy pattern matching is employed, already existing approaches use these fuzzy values only to find a single best-matching rule, thus immediately discretising the result. Our proposed approach differs in this regard, as we would like to use the fuzzy values resulting from pattern matching as inputs for our probabilistic model. To be more precise, we would like to treat these values as observations for the state estimation in the dynamic Bayesian Network. Our approach is, not coincidentally, similar to the methods typical for state and action recognition. This paper focuses on the Natural Language processing aspect, while the probabilistic model is described in a separate work.