Mid Sweden University

The rapid advancement of generative AI (GenAI) technologies presents unprecedented opportunities and challenges in higher education. Within the field of software development, much has been presented regarding GenAI for programming education. However, less has been investigated regarding the role of GenAI in requirements engineering. This study aims to explore the multifaceted impact of GenAI on the pedagogical processes of requirements engineering. It focuses on the student perspective, aiming to uncover how these technologies influence learning experiences, knowledge acquisition, and skill development. The goal is to understand their role in accurately and effectively defining software requirements. The research question that guided this study was: “What are the students’ perceptions of advantages and challenges associated with integrating generative AI into the educational practices of requirements engineering?”

This study was carried out with an Action research strategy as outlined by Norton (2009), where one of the authors had a dual role being both a researcher and a teacher in the course described below. Using a mixed-methods approach, this study integrates qualitative and quantitative data gathered from surveys, workshops, and interviews. Workshops were conducted within a requirement engineering course where students used the GenAI tool ChatGPT to generate and assess software requirements. This course was chosen because of its focus on requirements management and design principles for information systems, which offers a relevant context for exploring the integration of GenAI in the learning process for requirements management. These sessions, along with reflection activities and surveys, provide insights into AI's practical application in education. Qualitative data are analysed thematically to uncover students' perspectives, while quantitative analysis of survey data identifies patterns and preferences.

By examining both the potential benefits and limitations of generative AI in educational settings, this study contributes to a nuanced understanding of how such technologies can improve teaching methods and learning outcomes within requirements engineering. This study not only provides insights into the current state of AI in education, but also highlights the need for ongoing research to further refine how generative AI can be effectively integrated into software development education.