Artificial Intelligence and Machine Learning in Special Education: Enhancing Personalized Learning and Assistive Technologies for Diverse Learners

Abstract

Machine learning (ML) and artificial intelligence (AI) have become a game changer in the field of special education, being able to address the unique needs of learners with disabilities. The following are the research questions in this study: Can AI-based personalized learning systems and assistive technologies help improve educational outcomes, engagement, and accessibility? The study design was the mixed-method which involved quantitative research to examine the measures of learning performance as well as the qualitative data provided by learners and teachers. The results show that there are substantial positive changes in academic success, involvement in tasks, and usability of assistive technologies after introducing AI-based interventions. Individualized learning systems were highly adaptable, having the ability to adjust the instructional content to specific learner profiles, whereas AI-based assistive technologies enhanced communication, interaction and autonomy among the users. Moreover, machine learning models were highly predictive, which was conducive to effective and scalable learning environments. Regardless of these favorable results, issues of ethical implications, data confidentiality and accessibility are very critical. The paper sheds light on the significance of incorporating the pedagogical concept with the technological innovations in order to achieve successful adoption. In general, the study highlights the promise of AI and ML to achieve inclusive education through the development of adaptive and learner-centered learning settings that can accommodate various needs in education..