Abstract:

The advent of generative AI has become a topic of significant attention within education. The focus has mainly been on assessment and academic integrity however, GenAI’s (Generative artificial intelligence) capability to generate text, images, or other media, via generative models, has the potential to revolutionise AIEd (Artificial Intelligent Education) platforms to afford inclusion for students with special needs, previously been excluded from participation, especially in particular subject areas. Exclusion from mathematical equations, an essential component of education for students across all levels has been a particular issue as equations are not generally presented to students in a manner that suits their individual needs. For example, spatial representations and layout become a barrier for blind and visually impaired students. Dyslexic students can struggle with symbols but benefit from spatial representations. As generative models learn the patterns and structure of their input training data and then generate new data that has similar characteristics, when applied appropriately they have the potential to revolutionise education to adapt learning material to meet the particular needs of individual students and individual information processing capabilities and preferences. The authors explore the potential incorporation of GenAI in AIEd platforms to provide for inclusion of students with special needs in AIEd frameworks. The authors explore currently available GenAI tools to assess how they could potentially be applied for inclusion of vision-impaired and dyslexic students through content creation and adaptation to learner, capability and learning situation. The use of GenAI to facilitate individual information processing styles is considered. This paper will look at the benefits and challenges for inclusion of GenAI in AIEd framework development to provide a level playing flies for all students in solving accessibility issues with such frameworks.

Keywords:

GenAI, AI, Accessibility, HCI, Education, AIEd.