Universal Design Concept with Machine Learning for Developing Special Education

Abstract

          Universal Design for Learning (UDL) is an approach that creates learning environments that respond to the diverse needs of learners. This particularly includes students with special needs. In the digital age, technology and Machine Learning (ML) are used to help design and improve teaching and learning to make education efficient and appropriate. Machine learning-based systems can support universal instructional design for special education teachers in using data and algorithms to analyze and recommend appropriate teaching methods to suit individual students. This paper shows how machine learning can serve as a prototype system for special education teachers to analyze data and implement it in a real teaching environment. Its accessibility systems can help teachers design effective instruction based on information obtained from students with special needs. It is expected that the developed prototype of machine learning can help create educational equity in serving learners in special education for the betterment for all citizens.