Computational Architecture for Adaptation of Questions for Neurodivergent Students Based on Generative Artificial Intelligence Resources

• Júlio César da Costa Silva IFSULDEMINAS
• Hiran Nonato M. Ferreira IFSULDEMINAS

DOI: https://doi.org/10.5753/encompif.2024.2513

Abstract

This paper describes the development of an architecture, named Mind Bridge, to adapt assessment questions for neurodivergent students using Generative Artificial Intelligence resources. Mind Bridge aims to make questions more accessible and understandable, contributing to educational inclusion and equity of opportunities. The architecture was designed with three modules: Input Module, Logical Module, and Generative Module. The case study conducted demonstrated the effectiveness of the architecture in adapting questions from different knowledge areas, highlighting its ability to transform technical and complex questions into real-world problems.

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