Literador: A Comprehensive Tutoring System for Spanish Writing

Abstract

In a professional setting and in adult education, a well-written text needs to convey meaning by presenting ideas in a coherent and cohesive form that facilitates readability, such as the balance in the use of coreferences, the abstraction of the language used, and the lexical diversity of the text. In this work, we proposed Literador, an Intelligent Tutoring System for Spanish writing. By incorporating different Natural language Processing tools, Literador can analyze and provide feedback on the different aspects of a text, beyond just content. These tools are the Spanish-trained language model BETO, the text complexity analyzer Trunajod, and regular expressions to identify the use of key lexical elements. By combining all of these sources of information, Literador can provide feedback following a strategy that prompts different types of messages depending on the student’s level and tries, which also intends to avoid information overflow.