Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.

This industry experience report chronicles the experience of developing an impact-focused group project module within a computer science master's programme at King's College London over two years. The module was set up in collaboration with Amazon Web Services to match student teams with public sector challenges requiring innovative technological solutions. An iterative process of modifications based on partner and student feedback aimed to enhance the learning experience and outcomes. Key benefits included providing authentic professional development for students, enabling innovation and entrepreneurship, building partnerships between academia and the public sector, and embedding responsible innovation into projects. However, challenges emerged around managing expectations, ensuring consistent partner engagement, providing support for spin-outs, and handling sensitive data issues. As more projects involved artificial intelligence applications in the second year, developing mechanisms to ethically provide access while protecting sensitive information was an increasingly crucial need. Moreover, understanding the value and impact of this model of software engineering project module requires additional research support. Overall, this collaborative module offers a promising model to deliver impact-driven solutions through coordinating academia, industry, and public sector partners. Further research can help optimise such partnerships for societal impact.

Recent advances in artificial intelligence, specifically machine learning, contributed positively to enhancing the autonomous systems industry, along with introducing social, technical, legal and ethical challenges to make them trustworthy. Although Trustworthy Autonomous Systems (TAS) is an established and growing research direction that has been discussed in multiple disciplines, e.g., Artificial Intelligence, Human-Computer Interaction, Law, and Psychology. The impact of TAS on education curricula and required skills for future TAS engineers has rarely been discussed in the literature. This study brings together the collective insights from a number of TAS leading experts to highlight significant challenges for curriculum design and potential TAS required skills posed by the rapid emergence of TAS. Our analysis is of interest not only to the TAS education community but also to other researchers, as it offers ways to guide future research toward operationalising TAS education.