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.

Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This friction suppresses the iterative, exploratory nature of scholarly work. To investigate these challenges, we conducted an exploratory design study with 20 experienced researchers. This study identified key friction points: 1) the high cognitive load of managing iterative query refinement across multiple databases, 2) the overwhelming scale and pace of publication of modern literature, and 3) the tension between automation and scholarly agency.

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.

As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world, and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement and the implications for tertiary study, assessment and curricula in computing. "Work engagement", has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement. The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces.

Prior research has shown that storytelling is an effective method for increasing comprehension of concepts. Students often find computational topics, such as data structures, to be difficult to grasp initially. To bridge this gap, we investigate whether the use of storytelling in pre-lecture videos increases students' retention and understanding. At a North American university, instructors randomly assigned students to two separate groups who watch different types of pre-lecture videos: one in a traditional format and the other where they teach a concept through storytelling. These videos were deployed as unlisted YouTube links embedded in students' quizzes. Using YouTube's Reporting API, we analyzed the audience retention data against elapsed time to compare audience retention between traditional and storytelling teaching methodologies. There were more storytelling videos with a higher average retention level, and the audience displayed less skipping behaviour than their traditional counterparts. In the future we will further analyze students' perceptions of storytelling videos to better understand higher audience retention and the effectiveness of learning through storytelling lectures.