Enhancing Accessibility in Software Engineering Projects with Large Language Models (LLMs)

Digital accessibility ensures that digital products and services are usable by a diverse range of users, regardless of their physical or cognitive abilities. While numerous standards and guidelines have been established to aid developers in creating accessible content, studies reveal a persistent lack of accessibility in many web and mobile applications. This gap is often attributed to barriers such as lack of awareness, insufficient knowledge, absence of specific requirements, time constraints, and lack of executive support. In this context, we aim to address the lack of awareness and knowledge challenges by proposing a hands-on approach that leverages the capabilities of Large Language Models (LLMs) like ChatGPT to enhance students' accessibility awareness, knowledge, and practical skills. We engaged software engineering students in tasks involving website development and accessibility evaluation using checker tools, and we utilized ChatGPT 3.5 to fix identified accessibility issues. Our findings suggest that practical assignments significantly enhance learning outcomes, as interactions with LLMs allow students to develop a deeper understanding of accessibility concepts. This approach not only reinforces theoretical knowledge but also highlights the real-world impact of their work. The results indicate that combining practical assignments with AI-driven support effectively improves students' proficiency in web accessibility.