An Evaluation Rubric for Learning Management Systems: Enhancing Accessibility and Artificial Intelligence in Educational Settings and Industry Applications

Abstract

This dissertation by practice develops an innovative rubric (initially adapted from the Anstey and Watson model) for evaluating Learning Management Systems (LMSs) with a dual focus on Accessibility and Artificial Intelligence (AI), addressing current gaps in evaluation practices within educational and industry settings. The study utilises a mixed-methods approach, reviews existing literature, and integrates criteria underpinned by learning theory, culminating in the development of an enhanced LMS evaluation rubric.

Based on feedback from empirical testing, including surveys, observation studies, thematic analysis and decision analysis, the rubric was further refined, ensuring its relevance and effectiveness in addressing the specific needs of accessibility and AI integration in learning environments.

This research provides a robust tool for educators and industry professionals, proposing a standard for future evaluations that prioritise inclusive and technologically advanced learning environments. The implications extend to better-informed decisions in selecting and implementing LMSs, significantly influencing educational strategies and corporate training programs.