Generative AI and Large Language Models have become ubiquitous across education and within higher education institutions. Emerging challenges include potential over-reliance on Generative AI, risks to academic integrity, and inequitable access: there is an urgent need for students to develop ethical, self-regulated and grounded learning practices in its use. This paper presents insights distilled from a survey of 14 computer science educators in the UK, and identifies the overarching importance of teaching responsibility and ethical implications of the use of AI to students. The ANCOR framework is presented as a method for teaching responsible Generative AI use, integrating ethical reasoning, real-world examples, and curriculum-wide approaches. It offers a novel contribution by providing both actionable teaching techniques and a conceptual approach for embedding ethical and responsible use of Generative AI tools across computing curricula, including guidance on ethics integration, contextualising relevant policies, developing ethical decision-making skills, addressing anthropomorphism, and using illustrative real-world cases.

Purpose The purpose of this study was to explore how degree apprentices' career outcomes vary across their apprenticeship and beyond their degree award, both in economic terms and with respect to job satisfaction, looking specifically at outcomes based on gender and socio-economic status (SES). Design/methodology/approach We surveyed former, now graduated, apprentices across engineering and computing in seven universities in Scotland (n = 113). Survey questions were designed to ascertain career success across demographic groups and included wage uplift during and after their apprenticeship, and job satisfaction. Findings We found the majority of apprentices had moved into post-apprenticeship graduate-level roles with their apprenticeship employer, had enjoyed a considerable uplift in pay and reported high levels of job satisfaction. However, a concerning gender pay gap was found for women starting their new graduate careers. We also found significant pay differences based on SES with those with higher SES starting their apprenticeship on lower apprenticeship wages but then accelerating through pay scales more quickly after graduating. Originality/value This study provides a first exploration of career trajectories of those graduating from computing, engineering and built environment programmes looking at job mobility, pay and job satisfaction with a particular focus on gender and SES. This study fills an important gap in our understanding of the value of the degree apprenticeship in terms of career outcomes including salary uplift and job satisfaction.

This paper explores the use of AI-generated “Shadow Podcasts” as a supplementary learning tool in higher education. Developed using Google’s NotebookLM, the podcasts transformed lecture transcripts into short audio summaries, offering students an alternative means of engaging with course content. Deployed across five modules in two academic Schools within the Robert Gordon University, the podcasts were evaluated through a mixed-method survey of 85 students, combining quantitative ratings with thematic analysis of open-ended responses. Analysis suggests the podcasts were well-received, with most students rating them as good or excellent and noting improvements in their own engagement, understanding, and revision. The tool's conversational tone and ability to support multitasking distinguished it from traditional materials. Critiques centred on the artificial delivery, and lack of visual or detailed content. Students expressed interest in improvements such as more expressive voices, subtitles, video integration, and stronger alignment with assessments. The study highlights the potential of generative AI to enhance educational experiences when used transparently and reflectively. While Shadow Podcasts are not being positioned as a replacement for live teaching, they offer a flexible, scalable, and engaging complement to existing resources. Future work will involve cross-institutional research and further exploration of the podcasts’ impact on learning outcomes and curriculum design. This study contributes to the growing dialogue on pedagogically-grounded uses of generative AI in computing and business education.

CoCoNet brings together educators in HEIs with a shared interest in all aspects of individual final year projects. Our goal is to identify where innovations to practice are desirable, in the face of challenges provided by generative AI and increasing cohort sizes. Preliminary findings have shown significant variance in duration, weighting and assessment. Future work will bring to light commonalities and best practice to share across the UK Computing Community.

There has been a relentless march towards research-focussed papers in Computing Education conferences, influenced by Valentine's pejorative "Marco Polo" characterisation of practice papers. We argue that there is an important place for practice-focussed papers when presented in a format which provides careful contextual description, as exemplified by the work of seventeenth century ecologist Maria Sibyella Merian. Computing Education Practice (CEP) is a UK practice-based conference that has its tenth year in 2026. In this paper we discuss and describe the CEP reporting format, and review the character and contribution of the CEP series. We explore a) the structure and content of CEP papers in relation to papers at comparable conferences (Koli, ITiCSE and ICER) using Simon's classification system; b) what kinds of institution are represented at CEP; and c) CEP's influence on participants' teaching, through a (mostly) qualitative survey. We find that a) CEP papers are similar in scope, theme and context to other computing education conferences despite the nature of papers showing an expected and distinctive preponderance of reports; b) CEP is more representative of the full range of UK computing departments than comparator conferences, when considering average student entry standards; and c) participants identify value in participation and discussions as well as some specific contributions of the publications. We champion the value of carefully constructed reports of practice in computing education.

This Innovative Practice Full Paper outlines the findings of a survey conducted at the Robert Gordon University (Scotland, UK), focusing on feedback from faculty members and reflections subsequent to the introduction of a Pass/Fail grading system in the CS1 curriculum within the School of Computing, complementing previous work done focussing on the same implementation from the student perspective. This study aims to understand the impact of this grading model on teaching and assessment practices, student engagement, and motivation from the perspective of the module coordinators involved in the foundation year modules where this grading model was implemented. Analysis of the data indicates a generally positive reception of the Pass/Fail grading model among staff members. They reported streamlined marking processes and simplified grading grids as notable advantages. However, concerns were voiced regarding potential student demotivation and the ambiguity in determining the Pass/Fail threshold, which matched results from the student survey. Staff also encountered challenges in adapting assessment designs, particularly in shifting away from traditional grading paradigms. By shedding light on these observations, this paper contributes insights into the intricacies and consequences of integrating a Pass/Fail grading system into the early stages of an undergraduate computing curriculum. It not only underscores the need for careful consideration of pedagogical shifts but also provides valuable guidance for future implementation strategies. In summary, this research delves into the experiences and perspectives of staff members directly involved in implementing the Pass/Fail grading model. By addressing both the benefits and challenges encountered, it offers a comprehensive understanding of the implications of such a grading system within the context of undergraduate computing education. This, in turn, can inform decision-making processes and refine pedagogical approaches for enhanced faculty experience. Moving forward, exploring longitudinal effects of the Pass/Fail grading model on student retention rates could offer deeper insights into its efficacy in preparing students for future endeavors. Moreover, investigating potential variations in perceptions and outcomes across different academic settings and/or contexts could yield valuable comparative analyses.

The panel convenes five educators to discuss the ethical implications of utilising Generative AI (Gen-AI) and Large Language Models (LLMs) in computing education. Their expertise spans various domains, including organising national workshops on the implications of generative AI tools, conducting surveys on their use within curricula, implementing institutional policies related to technology use, and engaging with students directly in the classroom. They reflect on the evolution of Gen-AI and LLMs from challenging-to-use technologies to indispensable tools for users of all levels. Furthermore, they examine the ethical dilemmas arising from the widespread adoption of these technologies in educational contexts, particularly regarding issues of originality, integrity, and responsible use. In addition, they explore practical strategies for integrating ethics education into computing curriculum design and classroom practices. This includes discussions on the role of educators in guiding students towards ethical technology usage, addressing uncertainties surrounding Gen-AI tools, and fostering a culture of responsible innovation within educational institutions. Through their collective insights and experiences, the panel aims to provide recommendations for navigating the ethical complexities inherent in the integration of Gen-AI technologies into computing education curricula.

A RESEARCH ON THE USE OF LEARNING BY DEVELOPING ACTION MODEL IN COMPUTING STUDIES IN FINLAND AND THE UK HEIS

This Innovative Practice Full Paper investigates the implications of implementing a Pass/Fail marking scheme within the undergraduate curriculum, specifically across first year computing modules in a Scottish Higher Education Institution. The motivation for this implementation was to ease stress and pressure on students entering higher education, which became particularly relevant following the COVID-19 pandemic. The study reports on the results of a survey that gathered feedback from Stage 1 and Stage 2 students who experienced the Pass/Fail implementation, and results shows that students generally appreciate the Pass/Fail model, although for many, the benefits only become apparent once they are exposed to alternative grading models. A number of recommendations are made for the implementation of similar marking schemes within computing in Higher Education curricula.

In the UK context of an ageing population, degree apprentice-ships represent a new opportunity to study for a degree while working. Apprentices are full-time employees granted time to study for a degree with a significant workplace learning component. The aim of this study was to focus on whether degree apprenticeships are working for adult apprentices (aged 26 and up in this context). New apprentices ( n = 162) in six universities in Scotland, UK were surveyed to gain a better understanding of background, prior work and study experience, motivations and expectations. Results show that adult appren-tices join apprenticeship programmes with significant work experience and workplace metaskills, together with a consolidated sense of self as a professional. Adult appren-tices aimed to gain a degree while remaining in work, thus increasing skills in situ . The main barrier identified was the challenge of achieving work-study-life balance. The findings can be used to more closely align apprenticeship provision with adult apprentices’ skills needs while reducing barriers to accessing and succeeding in apprenticeships. We make recommendations for more flexibility in terms of advanced entry and Masters-level apprenticeships, with better recognition of prior experience, motivations and anticipated challenges.

This paper presents an experience report of online attendance and associated behavioural patterns during a module in the first complete semester undertaken fully online in the autumn of 2020, and the corresponding module deliveries in 2021 and 2022. The COVID-19 pandemic of 2020 resulted in a sudden move of most university teaching online, at a global and large-scale level. This, combined with the need to maintain “business as usual” resulted in new levels of student engagement data for largely unchanged pedagogical processes. Engagement data continued to be gathered throughout the subsequent, phased return to face-to-face and hybrid learning, although at a lesser level of granularity. The wealth of student engagement data gathered during this time allows quantitative insights into how student behaviour continued to adapt during and after the enforced online learning during the COVID-19 pandemic. The anonymous subjects of this case study are computing science students in their final year of undergraduate study. We examine their engagement with the virtual learning environment, including engagement with recorded lecture material, attendance in online sessions and engagement during in-person labs. We relate this to both the students' final grades and the content of the module itself. A number of conclusions are drawn based on this empirical data, relating to observations made by staff and pedagogical theory. There was a moderate, but significant, correlation between engagement in synchronous online lecture sessions and grades during thelockdown phase, but the strength of this correlation has reduced in subsequent years as normality has returned. From monitoring behaviour in online sessions down to minute-by-minute accuracy, it can also be seen that some students strategised their engagement based on sessions they perceived to be most directly contributory to their assessment, placing little value on live guest lecturer sessions. During enforced online learning, the most successful students, on average, engaged with less repeat content than less successful students, instead apparently utilising lecture recordings to “catch up” with missed live lectures.

Throughout the COVID-19 pandemic, institutions have had to shift and adapt rapidly to the ever-changing academic landscape. In response, several new, short-term initiatives were set in motion and new policies were, at least temporarily, adopted to support this rapid change. In computer science, specifically, there were new tools utilized, new modes of delivery employed, and new community engagement practices embraced. As a side effect of all this change, an evolving set of faculty and student expectations emerged. The results of two ITiCSE working groups, looking at the impacts of pandemic-related changes to the educational landscape from the perspective of faculty [1] and students [2], are presented. These are shared with an eye towards identifying strategic change and initiatives that might benefit the future of computing. Academic and administrative leaders within computer science share their thoughts regarding the future direction of computer science and strategic opportunities that can be leveraged. Through this session, a community of practice will be formed in order to catalyze knowledge sharing amongst leaders in computer science who are working to build upon the strategic initiatives that took place during this time of rapid change.

This panel convenes four educators, each from different institutions and each with experience managing group projects. Their expertise spans topics including: peer assessment and peer evaluation; entrepreneurship; transdisciplinarity; internationalisation; inclusivity; social values; educational technology and tools; feedback and feed-forward; peer rating; free-riders; as well as blended learning; and post-pandemic online discourse. They reflect on the delight of seeing students collaborate to deliver meaningful projects as well as the challenges posed by disengaged students. They also explore a common theme of discordance inherent to teamwork and systems to support student communities.

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 5404-5409ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1407Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain

This Research to Practice full paper presents pilot implementations of the Learning by Developing (LbD) at a higher educational institution in the UK as part of a projectbased module.The study analyses the students' experiences of LbD and perceived development of their competence via a selfassessment survey and presents these alongside interviews carried out with other stakeholders involved in these study modules (lecturers and project clients).The primary purpose of this study is to research whether the LbD pedagogical model is a suitable learning method for computer science students in a higher education context in the UK.The method has been used as a teaching method at Laurea University of Applied Sciences (Finland) since 2004 as part of its underlying strategic model.Still, little research has been carried out on the benefits of this pedagogical model outside the Finnish educational structure.LbD strives to develop students' general working life skills during their studies, which is essential in higher education today.LbD also emphasises the importance and value of continuous learning.

This article describes a study at Haaga-Helia University of Applied Sciences (Haaga-Helia) to understand how suitable the Learning by Developing (LbD) action model is as a teaching and learning method for computing students.The research also aims to obtain information about how the competence of calculation students in different competence areas develops during the study module selected for the research.In addition, we want to get information about the experiences of students, lecturers and customers about LbD pedagogy in general.The research result is also intended to be used in developing the LbD action model.

Throughout the COVID-19 pandemic, computing instructors have adapted their teaching to meet the needs of students in this ever-changing paradigm. These adaptations include the acquisition of new infrastructure, evolving expectations, revised course development strategies and the adoption of new modes of course delivery. Last year, an ITiCSE Working Group, led by Siegel and Zarb, explored the ways in which higher education computing faculty responded to this dramatic shift in the (educational) world [1]. Based on a survey of computing faculty worldwide, the work explored what the academic landscape might look like beyond the impacts of COVID-19. We will explore these results and discuss lessons learned along with the evolution that has taken place since the survey data was collected. Recognizing that it is unlikely that we return completely to pre-pandemic norms, our goal is to identify practices within computing that have newly formed or improved throughout the pandemic, giving extra weight to those that we might hope to keep as we move into a post-pandemic future.

Throughout the COVID-19 pandemic, computing instructors have adapted their teaching to meet the needs of students in this ever-changing paradigm. These adaptations include the acquisition of new infrastructure, evolving expectations, revised course development strategies and the adoption of new modes of course delivery. Last year, an ITiCSE Working Group, led by Siegel and Zarb, explored the ways in which higher education computing faculty responded to this dramatic shift in the (educational) world [1]. Based on a survey of computing faculty worldwide, the work explored what the academic landscape might look like beyond the impacts of COVID-19. We will explore these results and discuss lessons learned along with the evolution that has taken place since the survey data was collected. Recognizing that it is unlikely that we return completely to pre-pandemic norms, our goal is to identify practices within computing that have newly formed or improved throughout the pandemic, giving extra weight to those that we might hope to keep as we move into a post-pandemic future.

Students have experienced incredible shifts in the in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic presses on, students have had to repeatedly adapt to the continuously evolving educational landscape as this global health crisis forced an "unprecedented global shift within higher education in the ways that we communicate with and educate students". This working group builds upon foundations and structure created by a 2021 ITICSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That Working Group identified the incorporation of some pandemic-induced changes into future teaching practices. In this Working Group, we explore existing literature regarding the student experience in response to the evolving teaching practices catalyzed by COVID-19). Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We explore how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their practices going forward.

Students have experienced incredible shifts in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic pressed on, students have had to repeatedly adapt to the continuously evolving educational landscape. This working group builds upon foundations and structure created by a 2021 ITiCSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That working group identified the incorporation of some pandemic-induced changes into future teaching practices. This working group examines the existing literature and insights gained from responses to a multi-national survey to explore the new student experience emerging from the continuously evolving teaching practices catalyzed by the global pandemic. Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We investigate how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their educational methods going forward.