Automatic content adaptation in interactive environment for individualized learning

Abstract

Intelligent tutoring systems have been highlighted as a tool for learning support, mainly because its adaptation to user’s condition and application scenario.
In the face of this pandemic situation, learning supporting tools, as tutoring systems, because of their viability for application in remote environment, showed as an alternative for professors and students support. This paper addresses a proposal for content adaptation to learning, applying artificial intelligence techniques to text Generation and natural language processing. Furthermore, this paper considered existence for different learning styles, varying according to student personality and his way to collect information, capable to influence in learning level. This style variation, accordingly to a model defined in literature, directs content adaptation model to be done, generating an adapted result to each learning style.
Personalized content generation process allows an advance in way to use tutoring systems in learning, creating a format for directed content delivery with gains for environment’s user, generating a personalized results and conducting to a more effective learning. As a result, expect to obtain a methodology to content generation based on pre-defined learning styles, by mean an automatized process to content adaptation from an initial entry in unique format.