Abstract Software engineering education widely adopts Agile and project-based learning approaches. However, questions remain about how student roles, perceptions, and the use of Large Language Models (LLMs) relate to learning outcomes. In this exploratory study, we investigate how students engage in a semester-long project-based agile course by examining three core elements: their software development roles and Scrum roles, their expectations and actual use of LLMs, and their perceptions of teamwork and learning practices. We focus on differences between below-average and above-average performing students, thereby linking project outcomes to effort, perceptions, and role-specific engagement. Our analyses show that student effort is strongly associated with performance, and that the distribution of typical software development roles relates to meaningful differences in both engagement and outcomes. The findings show that front-end developers tend to exhibit lower engagement and performance, indicating a need for role-specific support. Although students were expected to use LLMs, actual usage was limited, especially in complex tasks like testing and deployment, revealing a clear expectation–use gap rather than allowing conclusions about its underlying causes. Higher-performing students reported more positive perceptions of team collaboration and Scrum practices, suggesting a link between engagement, perception, and outcomes. Together, these findings contribute to understanding how roles, perceptions, and LLM expectations interact with student performance in agile, project-based settings.

Abstract Software engineering involves more than coding. It encompasses planning, development, communication, and process management. Scrum, the most widely adopted agile methodology, helps teams deliver value iteratively, yet practitioners often struggle with challenges such as maintaining requirement clarity, reducing cognitive load, and managing communication overhead. As artificial intelligence (AI) becomes increasingly integrated into the software engineering lifecycle, its potential to improve productivity, quality, and decision-making is gaining significant attention. Moreover, Scrum offers a structured yet flexible framework, but it remains unclear which AI features can most effectively support its practices in real-world settings. Therefore, this study addresses that gap by identifying and prioritizing key Scrum AI Support Features (SAISFs) based on industry needs. A two-phase research approach was used. First, a focus group with five software engineering industry experts identified 18 relevant SAISFs. Second, a survey using the Kano methodology was conducted with 344 experienced Scrum practitioners to evaluate and prioritize these features. The results were analyzed across three Scrum team size groups: small ( < = 6), medium (7–10), and large (11+), and four functional SAISF groups: Requirements Support (R), Development Support (D), Communication Support (C), and Scrum Process Support (S). The research also provides prioritization of SAISFs according to Scrum roles. Our findings offer actionable insights for designing AI-enhanced tools tailored to Scrum teams, highlighting the importance of considering team size and Scrum roles when prioritizing AI features. This study contributes to the agile software engineering literature by offering a practitioner-informed foundation for integrating AI into Scrum-based project environments. Future Scrum tools may become adaptive and context-aware, automatically tailoring workflows, predicting bottlenecks, and optimizing team communication and performance.

Information security is one of the key areas of consideration to assure reliable and dependable information systems (IS). Achieving an appropriate level of IS security requires concurrent consideration of the technical aspects of IS and the human aspects related to the end users of IS. These aspects can be described in the form of information security requirements. We propose an approach that helps select and balance information security software requirements (iSSR) and information security training requirements (iSTR) according to the information security performance of end users. The approach was tested in an experiment involving 128 IS professionals. The results showed that using the proposed approach helps IS professionals with limited experience in information security make significantly better decisions regarding iSSR and iSTR.

Cybersecurity is a hot topic and researchers have published extensively on studies conducted using a variety of different research methods. This paper aims to determine which qualitative research methods were most used and for studying which topics. A systematic literature review on Web of Science, Scopus and ACM DL has been conducted to achieve an overview of quantitative methods used in cybersecurity. The review covered the most recent research in different areas of cybersecurity (i.e., personal, organizational and state cybersecurity) in the period of 2017 to 2019. After careful inspection of papers, we identified 160 papers reporting on the use of qualitative methods. The most common qualitative methods are interviews, followed by case studies and observation. Other studied qualitative methods (i.e., focus groups, grounded theory, action research and Delphi method) seem to be much less frequent. Although qualitative methods are used when studying all key cybersecurity areas, they often lack the necessary rigor and detail observed in other research areas where qualitative methods are well-established.