Individualized content adaptation model based on learning styles

• Fernanda F. Peronaglio São Paulo State University “Júlio de Mesquita Filho” http://orcid.org/0009-0000-4218-2090
• Aleardo Manacero Jr. São Paulo State University “Júlio de Mesquita Filho” https://orcid.org/0000-0002-4581-7482
• Alexandro J. Baldassin São Paulo State University “Júlio de Mesquita Filho” https://orcid.org/0000-0001-8824-3055
• Matheus S. dos Santos São Paulo State University “Júlio de Mesquita Filho” https://orcid.org/0000-0003-4849-1649
• Renata S. Lobato São Paulo State University “Júlio de Mesquita Filho” https://orcid.org/0000-0001-8248-0826
• Roberta Spolon São Paulo State University “Júlio de Mesquita Filho” https://orcid.org/0000-0003-3164-2658
• Marcos A. Cavenaghi Humber Institute of Technology and Advanced Learning https://orcid.org/0000-0002-5653-9959

DOI: https://doi.org/10.5753/sbie.2024.242720

Abstract

The use of intelligent systems has expanded notably since the introduction of new machine learning techniques, a trend further reinforced by the emergence of Large Language Models (LLMs). This growth has also been observed in education, an area where Intelligent Tutoring Systems have been introduced for quite some time. In this context, an interesting application is the generation of content tailored to learning styles, where educational material is produced in a customized manner for each category of student. Presented here is a tool that uses artificial intelligence techniques to construct content adapted to the Learning Styles Inventory created by David Kolb. This tool automates the production of specific content for each style based on a base text provided by the teacher. The results obtained demonstrate that using LLMs enables the creation of specific texts with ease, allowing teachers to produce texts adapted to each student's profile.

Keywords: Intelligent Tutoring Systems, Content Generation, Text Generation, Artificial Intelligence

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