Agentic AI, where AI systems have their own forms of agency, will present some of the most critical challenges that computing education will face, including regulatory gaps and amplified cascading social and ethical impacts. This working group proposes a landscape analysis of the ethical and societal implications of using Agentic AI in higher computing education. By exploring emerging literature and use cases of Agentic AI, we aim to contribute a timely landscape study exploring 1) the emerging challenges and opportunities associated with Agentic AI, 2) how higher education is beginning to adopt and use Agentic AI and the resulting ethical and societal impacts, and 3) the implications (e.g. challenges, opportunities, limitations) of integrating Agentic AI in computing education. The expected outputs are: 1) a protocol and literature scoping review of Agentic AI in education, and 2) an analysis of current practices and use cases in computing education. Together, these outputs aim to identify emerging patterns, use cases, key challenges, and principles to support the ethical and responsible integration of Agentic AI in education.

This study examines the development of reflective practice among students in an undergraduate work-based Software Engineering program. Using two established models of reflection - Boud et al.'s Model of Reflective Process and Bain et al.'s 5R Framework for Reflection - we analyse a series of reflective reports submitted by students over four years. Our longitudinal analysis reveals clear trends in how students' reflective abilities evolve over the course of the program. We find that more sophisticated forms of reflection, such as integration of knowledge, appropriation of skills, and reconstruction of practice, increase markedly in prevalence in later years. The complementary nature of workplace experience and university study is highlighted in students' reflections, demonstrating a key benefit of the work-based learning approach. Our findings provide insight into how reflective practice develops in Software Engineering education and suggest potential value in incorporating more structured reflection into traditional degree programs.

The software engineering graduate apprenticeship program at the School of Computing, University of Glasgow, has a significant emphasis on work-based learning (WBL), with approximately 80% of the students' time over four years spent in the workplace. This work-based aspect of the program plays a more prominent role in the final two years, by which time apprentices are undertaking increasingly larger roles in the workplace. In this report, we present how we addressed the challenge of structuring and assessing WBL in these senior years such that there is a balance between professional competency attainment and ranked academic achievement, while providing a fair and flexible structure. Based on a model of WBL presented by Raelin in 1997, we outline our rationale for dividing the assessments into workplace projects, workplace journal, and a portfolio of artefacts. The projects are categorised into different types both for flexibility and to encourage academia-industry collaboration, the workplace journal encourages higher forms of reflection, while the portfolio assessment encourages and assesses the attainment of professional competencies. We present our experience of implementing this structure, an analysis of student feedback, and the adjustments made in response. With an increasing focus on work-ready skills and competency-based education in software engineering education, we expect the theory-informed structure we developed, and our experience of running and adapting it, to serve as an exemplar for developing a WBL program at research-led institutions.

Work-based learning (WBL) programs in Software Engineering provide students with opportunities to apply academic knowledge in the workplace. However, how students navigate the transition between knowledge and skills acquired at university and their application in the workplace remains largely unexplored. We implemented a WBL Software Engineering program in which students spend most of their time in the workplace rather than at university. Reflective student essays were used to examine how our implementation supports the integration and transfer of learning between academic and professional contexts. University modules - such as those on software design patterns and programming - offered directly applicable skills, while foundational knowledge and problem-solving frameworks supported further development. Workplace experiences reinforced understanding, provided context, and enabled practical application. However, skills such as project management, leadership, and context switching were primarily developed in the workplace, revealing gaps between academic team projects and industry needs. Difficulties in transferring some advanced technical skills also point to a disconnect between academic instruction and industry application. Our findings suggest a cyclical relationship between university and workplace learning, with each enhancing the other. We propose key considerations for WBL program design, including curriculum alignment, structured skill development, and integrated delivery. Finally, we underscore the value of reflective essays in observing and supporting skill integration.

Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. This working group will consider the ethical and societal impacts of GenAI in higher computing education. In this paper, we outline the goals, methodology and expected deliverables of the working group. In particular, we will carry out a systematic literature review to address a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and social impacts, we will provide an evaluation of university policies on the adoption and guidelines for use of GenAI for computing education and develop a framework to outline the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for educational and professional codes of ethics.

Competencies may be defined as the knowledge, skills, and professional dispositions that an individual is required to demonstrate in order to be considered professionally competent. Competency-based education has long been a feature of professional degree programs, but the discipline of Computing Science has only recently begun to embrace competencies as a means of structuring or evaluating students' learning. Meanwhile, the practice of work-based learning - also well-established in other professional disciplines - has become more prevalent in Computing Science education, with increasing emphasis placed on work-based modes of learning, such as internships and apprenticeships. In this paper, we examine how students enrolled on a degree-level apprenticeship in Software Engineering have developed their professional competencies in the workplace. The paper is based on an analysis of 38 student assignments, wherein apprentices were asked to identify the competencies they have demonstrated, with reference to a portfolio of work. The UK Standard for Professional Engineering Competence and Commitment, which outlines the competencies required for certification as an Incorporated Engineer, provided the necessary framework. Competencies relating to communication and inter-personal skills were among those most often cited by apprentices, with competencies relating to knowledge and understanding and design and development systems also featuring prominently. Competencies relating to responsibility, management, or leadership were less prevalent, with professional commitment proving to be the least commonly cited category of competencies. We provide examples of how apprentices claim to have demonstrated each competency, and discuss the implications of these findings for competency-based learning in Computing Science education.

Equality of access to higher education is a priority for governments the world over. For example, the United Nations Sustainable Development Goal relating to Quality Education includes a target to “ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university” by 2030 (United Nations, 2022). For those from areas of relative socioeconomic deprivation, the cost of studying for a degree qualification represents a barrier to participation. As such, apprenticeships and similar forms of work-based learning are cited as a means of overcoming this barrier, offering students an opportunity to earn a wage while pursuing their degree. The work described here explores how - and to what extent - a work-based degree in Software Engineering has facilitated wider participation in higher education, through a series of in-depth interviews with apprentices from relatively deprived backgrounds. Analysis suggests that economic considerations are chief among the concerns of apprentices from areas of deprivation; however, the relevant factors are evidently intersectional in nature, and often intertwined with a wider range of personal circumstances, including the effects of previous choices about education and work. As such, we believe this work has implications for how we understand the potential for work-based degrees in Software Engineering and Computing Science to widen access to higher education, and for universities to contribute to achieving the United Nations Sustainability Goal of affordable and quality technical, vocational, and tertiary education.

We have seen students struggling with fully understanding the algorithms when required to trace pseudocode for an Algorithmics course, even though examples and exercises on tracing each algorithm are provided throughout. This course is being taught in year 2 of the new Graduate Apprenticeship in Software Engineering programme (GA) [2], covering fundamental string and graph algorithms and basic computability topics.

Probabilistic model checking can overcome much of the complexity inherent in balancing games. Game balancing is the careful maintenance of relationships between the ways in which a game can be played, to ensure that no single way is strictly better than all others, and that players are offered a wide variety of ways to play successfully. We introduce a novel approach toward automating game balancing using probabilistic model checking called chained strategy generation (CSG). This involves generating chains of adversarial strategies, which mimic the way players adapt their approach during repeated plays of a game. We use CSG to map out the evolving metagame. The trends identified can allow game developers to identify strategies, which will be too strong, and ways of playing the game, which a player may want to use, but are never viable for successful competitive play. We introduce a case study, a game called RPGLite, and use CSG to compare five candidate configurations for the game. We show how to determine which configurations of RPGLite lead to a more fair and interesting experience for players. We also identify unexpected trends in how the strategies evolve. Our approach introduces a new technique for improving game development and player experience.