A methodological approach to enable natural language interaction in an Intelligent Tutoring System.

In this paper, we present and evaluate the recent incorporation of a conversational agent into an Intelligent Tutoring System (ITS), using the open-source machine learning framework Rasa. Once it has been appropriately trained, this tool is capable of identifying the intention of a given text input and extracting the relevant entities related to the message content. We describe both the generation of a realistic training set in Spanish language that enables the creation of the required Natural Language Understanding (NLU) models and the evaluation of the resulting system. For the generation of the training set, we have followed a methodology that can be easily exported to other ITS. The model evaluation shows that the conversational agent can correctly identify the majority of the user intents, reporting an f1-score above 95%, and cooperate with the ITS to produce a consistent dialogue flow that makes interaction more natural. • Conversational toolkits provide the required support to develop C-ITS. • Relatively high performance is obtained by using spaCy featurization along with DIET. • Methodological guidelines provided can easily be exported to other different settings. • There is a compromise between time performance and accuracy. [ABSTRACT FROM AUTHOR]