The AI Scaffold and Engagement Spectrum as a Novel UDL-Aligned System for Supporting Students with Dyslexia

Artificial Intelligence (AI) is rapidly changing higher education, offering new opportunities to support diverse learners, including students with dyslexia, while raising important questions around ethics and pedagogy. This study introduces a novel, research-informed framework that supports students with dyslexia in reading, writing, and academic workflow planning, grounded in the principles of Universal Design for Learning (UDL) and constructive alignment. Using mixed methods, including student surveys and interviews with the Student Support and Wellbeing Services (SSWS) team, we identify key barriers such as information overload, linear writing demands, and uncertainty around AI use. The framework offers a step-by-step guide for responsible AI use aligned with UDL's principles of multiple means of engagement, representation, and expression. This paper’s primary contribution is a novel, dual-component pedagogical system designed to resolve this uncertainty. It comprises an academic-facing model, \textit{the AI Engagement Spectrum}, which shifts responsibility for ethical AI use from student policing to proactive instructional design, and a student-facing workflow, the \textit{AI Scaffold} that applies UDL principles for AI-supported academic tasks. The framework provides a process that embeds ethical checkpoints designed to support AI literacy, ensure human-in-the-loop agency, and mitigate the risks of uncritical cognitive offloading. Our findings suggest that with the right design, AI can become a practical and equitable support strategy, enhancing autonomy while addressing concerns like skill atrophy and academic integrity.