Decision Trees as a Tool for Promoting Inclusion and Diversity in Adaptive Education

Inclusion in education is critical to achieving Sustainable Development Goal 4, which ensures equitable education for all. This study explores the application of decision tree algorithms to improve adaptive education, promoting inclusion and diversity in higher education. Decision trees were used to classify students into predefined categories based on various attributes such as gender, age, learning styles, and specific needs (for example, ADHD). The algorithm was trained on an experimental dataset and demonstrated high accuracy (95.94%) in classification. By dynamically adjusting educational content and accessibility options, this approach aims to provide personalized learning experiences tailored to individual needs. The findings highlight the potential of AI-driven adaptive learning systems to improve educational outcomes and foster an inclusive environment by accommodating various learning preferences and requirements. This research contributes to a greater understanding of how technology can be used to address educational inequities and suggests future directions to improve adaptive learning frameworks.