Innovative Use of Generative AI for Content Simplification in Special Education Settings

Abstract

The provision of individual and cognitively stimulating learning resources to children with intellectual disabilities, autism spectrum disorders, dyslexia or language processing difficulties is often difficult in a special education context. Traditional digital learning platforms have a low degree of flexibility to adapt the learning material to fit the needs of each student and consequently, their engagement and learning effectiveness is reduced. To tackle this issue, the researchers present an innovative generative artificial intelligence framework, called EduSimplify-GAI, for the problem of adaptive content simplification for inclusive learning environments. The proposed framework combines the following components: NLP, transformer based semantic analysis, reinforcement guided readability optimization, and context-aware restructuring of the text to produce simplified instructional content without compromising the educational content. It is based on the generative AI model, GPT-4, along with the BERT model, which is a semantic validation module, and an adaptive readability scoring mechanism. The datasets employed in the experiment were multilingual educational datasets, and special education learning materials were used in the experimental tasks in reading comprehension, vocabulary adaptation and conceptual simplification. The proposed EduSimplify-GAI model recorded 96.4% accuracy of simplification, 94.1% retention of the meaning, 92.8% improvement in comprehension of the learner, and 91.7% teacher satisfaction which was around 13-18% higher when compared to the conventional rule-based or standard NLP models. This is the main contribution of this work, where an adaptive pedagogical assistance framework using generative AI is dynamically tailored to learner cognitive profile, to customize the educational content. The novelty of the study is the combination of generative AI and semantic preserving readability optimization for the special education applications. The findings show that leveraging generative AI can greatly enhance accessibility, inclusion, and the effectiveness of personalized learning in today's special education system.