Relieving instructor angst about inclusive design

Exploring the potential of gen AI to sustainably support the implementation of Universal Design for Learning

Abstract

The literature has established that if instructors shy away from implementing Universal Design for Learning (UDL) it is often because of lucid and tangible fears about workload.  In parallel, the emergence of gen AI has immediately triggered hopes that large language models (LLMs) might be successfully used to support the challenging planning tasks of higher education instructors; it has immediately struck scholars that this might include supporting instructors who might have fears about workload and competencies in relation to UDL implementation in their classes.  This study explored the degree to which an LLM could be effective in supporting an instructor redesign two Masters of Education courses in order to make them more aligned with UDL than they already were.   The theoretical lens used in this project was the social model of disability.  The methodological framework used was action research.  The research team prompted the LLM for UDL strategies.  A process of triangulation invited students having previously taken the courses to assess the effectiveness of the redesign. The findings suggest that gen AI can indeed support the UDL redesign of courses.  Concerns are, however, raised because mastering the prompting competencies necessary may be as complex as the UDL redesign itself.