The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students’ learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e. individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students’ comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Abstract In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the application of large language models (LLMs) to improve the quality of user stories within the agile teams of Austrian Post Group IT. We developed an Autonomous LLM-based Agent System (ALAS) and evaluated its impact on user story quality with 11 participants from six agile teams. Our findings reveal the potential of LLMs in improving user story quality, provide a practical example, and lay the foundation for future research into the broad application of LLMs in a variety of industry settings.

Open-source software (OSS) development has become increasingly influential in the software industry, promoting collaboration and knowledge sharing among developers and users. Along with rapidly evolving OSS projects, this paper explores requirements engineering (RE) practices and challenges through a systematic literature review (SLR). Synthesizing data from 43 selected papers, the study reports practices, techniques, and methods that assist RE activities in OSS projects, and also addresses challenges faced by practitioners and the potential solutions. The results of the literature review indicate a growing interest in using machine learning and statistical methods to assist RE activities, focusing on automated requirements identification and analysis using information from project discussion forums, issue reports, and other online resources. The findings also highlight the importance of community involvement, with many studies examining developers’ interaction patterns, expertise levels, and influence on projects. These findings provide valuable insights for OSS project managers and researchers, offering guidance on effectively handling requirements in OSS projects.