Empowering Diversity by Building Inclusive Software Engineering Projects with Large Language Models

Abstract

The integration of emerging AI technologies, particularly Large Language Models (LLMs), is fundamentally reshaping the landscape of software engineering. LLMs offer a wide array of capabilities that can enhance various aspects of software development, including design assistance, automated code analysis and synthesis, and testing. Consequently, their integration into software engineering practices holds the potential to greatly improve efficiency and code quality, marking a notable paradigm shift towards the creation of more intelligent and adaptive systems. This allows the definition of an extended perspective when establishing requirements for building software engineering solutions by incorporating evidence from literature resources and practice. By synthesizing requirements and evidence from scholarly and practitioner sources using LLMs, software engineers can ensure that their solutions are not only technically sound but also align with best practices in fostering social norms and values like inclusivity. Adopting such an approach supports the creation of responsible and inclusive educational software that caters to diverse learning needs and promotes equitable access to educational resources. Moreover, by using LLMs to inform decision-making throughout the software development lifecycle, software engineers can iteratively refine and enhance their solutions based on emerging research findings, thereby ensuring continuous improvement in fostering inclusive educational environments. Hence, this research aims to develop a novel evidence-based software engineering method informed by insights from scientific literature. As a use case, we design and implement a dyslexia-oriented educational software application that supports children in learning to read, guided by this new methodological approach.