This innovative practice paper presents a work-inprogress on the design of a 'play-kit' to introduce incoming first-year university students to diverse thinking styles through playful interactions, addressing the need for adaptable problemsolving skills development required to tackle increasingly complex global socio-technical challenges. Our initial design stage involves creating a prototype physical workbook to stimulate computational thinking skills through play. We will adapt lessons from existing computational thinking material, originally designed as a classroom-based tool for primary school students. We customize lessons for university students, and re-work them so that they become self-directed learning activities. Our workbook emphasizes essential computational components - decomposition, algorithms, pattern recognition, logic, representation, and abstraction. In time, the project will offer both physical and online 'Learning to Think' play-kits to widen accessibility and suit a diversity of learning styles.

This paper presents the design and implementation of a two-week hackathon at a large prestigious UK university, focused on creating sustainable solutions leveraging generative artificial intelligence (genAI). The hackathon deviated from the traditional one to three-day format, providing an extended period for ideation, development, and public voting. The event aimed to foster innovation, engage the local community in sustainability efforts, and augment participants' problem-solving capabilities through the use of genAI. The hackathon incorporated inclusivity measures based on evidence-based recommendations, ensuring diverse participation and a supportive environment. The participants could choose from four challenges to create a solution around that were based on local sustainability issues: housing regeneration, promoting sustainable and active travel, revitalizing the city center, and increasing re-naturing within the city. Teams were formed using a mix of self-selection and pre-assignment. Participants had access to comprehensive resources, including workshops on jupyter notebooks, genAI, video creation, and support from mentors. The final outputs each team was expected to produce was a 90 second video that detailed the challenge, their proposed solution, how they used open data, how they used genAI either in their solution or in their work process, as well as any files required for their solution to run/compile. The video was also to be used for the public vote, to decide the people's choice award, and hence could be promotional in nature but everyone in the team had to contribute to it in a meaningful way. Thus, they do not have to appear in it but could write a script or edit, etc. Judging criteria focused on the quality of the presentation, creativity, effective use of open data, and engagement with genAI. The event concluded with awards for the most polished solution, most creative idea, best use of open data, best use of genAI, best overall, and a people's choice award decided by a public vote. This paper contributes to the body of knowledge on leveraging genAI for sustainability and offers insights into develoning inclusive and impactful hackathons.

This innovative practice full paper describes an indepth analysis of the pedagogical implications of incorporating generative artificial intelligence (genAI) tools, specifically Chat-GPT, into a data science course for postgraduate masters computing students. This research is grounded in the implementation of ChatGPT in a data analysis course, aiming to evaluate its effectiveness in fostering students' analytical and decision-making capabilities. The study employs a qualitative methodology to assess the educational outcomes of integrating ChatGPT, focusing on its impact on student engagement, learning efficiency, and the development of critical thinking skills in the context of data science. Through a combination of interviews, and analysis of students' project outcomes, we gather insights into the challenges and opportunities presented using genAI in the data science course. A notable innovation of our approach is the introduction of a dual-report assessment method, which not only evaluates the students' project results but also their proficiency in prompt engineering - a crucial skill for effective interaction with genAI tools. Our findings suggest that while students demonstrate enhanced data analysis skills, they also face difficulties in accurately framing queries to yield useful results from genAI, highlighting an essential area for further curriculum development. Further-more, the work delves into the pedagogical strategies that can optimize the benefits of genAI tools in education. It emphasizes the importance of a structured framework that guides students in the ethical use of genAI, encourages critical reflection on AI-generated content, and fosters a deeper understanding of the underlying algorithms and their implications for data science. The implications of this research extend beyond the classroom, offering valuable insights for instructors, curriculum developers, and policymakers on integrating AI technologies into educational practices. By providing a comprehensive overview of the benefits and challenges associated with the use of ChatGPT in data science education, this paper contributes to the ongoing dialogue on preparing students for a future where genAI might a significant role. In conclusion, this work highlights the potential of genAI to revolutionize data science education by enhancing analytical skills and decision-making capabilities. Continued exploration of effective strategies for integrating AI tools into learning environments, such as data science, is required to ensure that students are equipped with the knowledge and skills necessary to navigate the complexities of genAI for future employment.

This innovative practice full paper describes the development and implementation of a cross-discipline techno-preneurship course, highlighting best practice, the benefits of the course perceived by students and further considerations. The course is offered at a large prestigious UK university in a department where electronic, electrical engineering and computer science courses are taught, and is mandatory for the electrical and electronic engineering students and elective for computer engineering students, who are in their third year of study as part of combined bachelor's and master's degree programs. It emphasizes teamwork, problem identification, problem solving, and creativity. This course uniquely integrates engineering skills with entrepreneurship, using a project-based learning approach requiring students to work in teams to develop a pitch, business plan, technical feasibility study, and a working prototype. These have been shown to be the most predominant methods to assess technopreneurship courses. However, the course is set apart by the focus on real-world world problems and fostering connections between students and the local start-up ecosystem. A key strength of the course is to improve students professional skills, which has been shown to be desired by employers in industry. In this work, we outline the course structure, intended learning outcomes, assessment, schedule of teaching, and present findings gained from teaching the course. Therefore, it is easily replicable by other practitioners. We detail how this builds upon previous practices to further the aims of the course to increase links with the local start-up ecosystem and improve students professional skills. The results of an online questionnaire proposed by the University optionally completed by students at the end of the course in the 2022/23 and 2023/24 academic years, revealed that students do perceive the benefit of the course as a way to develop their professional skills, such as public speaking, teamwork, and writing skills. Furthermore, the students appreciated the course structure and felt well-informed about the assessment. The results also revealed that the students rated the course highly for overall quality. The work produced by the teams confirmed our hypothesis that the hardware and/or software nature of their prototype/product appears to be disconnected from the makeup of students from the two different program backgrounds enrolled on the course. For instance, a team of only electronic engineering students still had a highly important software component that was vital for their final product. However, even more interestingly, the success of each team would appear to be based upon the team dynamics, which was monitored by the academic instructor in chard of the course throughout the entirety of the course and was a part of the assessment. Teams that demonstrated good levels of teamwork, overall tended to do better in the course than teams, which did not.

We present a simulation framework for evaluating the performance of cooperative reconfigurable intelligent surface (RIS) based systems, which may ultimately deploy an arbitrary number of RISs to overcome adverse propagation-related effects, such as cascaded fading. The physical model underlying the proposed framework considers the (optional) presence of a dominant signal path between the source and RIS, and then between each subsequent stage of the communication link to the destination. Accompanying the dominant signal component is a non-isotropic scattered signal contribution, which accounts for angular selectivity within the cascaded RIS stages between the source and destination. The simulation of the time-correlated scattered signal, reflected by the illuminated reflective elements, is achieved using autoregressive modelling. As a by-product of our analysis, significant insights are drawn which enable us to characterize the amplitude and phase properties of the received signal, and the associated complex autocorrelation functions (ACFs) for the product of multiple Rician channels. For both single and cooperative RIS systems, the outage probability (OP), and important second-order statistics, such as the level crossing rate (LCR) and average outage duration (AOD), are analyzed for a variety of system configurations, accounting for practical limitations, such as phase errors. It is shown that by using multiple RISs cooperatively, the AOD is reduced at a lower signal-to-noise-ratio (SNR) compared to single RIS-assisted transmission under the same operating conditions. Lastly, increased channel variations (i.e., higher maximum Doppler frequencies) are shown to decrease the AOD in the case of absent phase errors; yet, this improvement is not observed when phase errors are present.

This contribution proposes a simulation framework for quantifying the performance of employed reconfigurable intelligent surface (RIS) based systems to overcome adverse propagation-related effects. The physical model underlying the proposed framework considers the presence of a dominant signal path between the source and RIS, and then between RIS and the destination. The simulation of the time-correlated scattered signal reflected by the illuminated reflective elements is achieved using autoregressive (AR) modeling. As a by-product of our analysis, significant insights are developed which allow for the characterization of the amplitude and phase properties of the received signal, and the associated complex autocorrelation function (ACF) for the product of two Rician channels. Capitalizing on this, we derive the corresponding first and second order statistics, which lead to the development of useful theoretical and practical insights.

The modeling of wireless communications channels is often broken down into two distinct states, defined according to the optical viewpoints of the transmitter (TX) and receiver (RX) antennas, namely line-of-sight (LoS) and non-LoS (NLoS). Movement by the TX, RX, both and/or objects in the surrounding environment means that channel conditions may transition between LoS and NLoS leading to a third state of signal propagation, namely quasi-LoS (QLoS). Unfortunately, this state is largely ignored in the analysis of signal propagation in wireless channels. We therefore propose a new statistical framework that unifies signal propagation for LoS, NLoS, and QLoS channel conditions, leading to the creation of the Three State Model (TSM). The TSM has a strong physical motivation, whereby the signal propagation mechanisms underlying each state are considered to be similar to those responsible for Rician fading. However, in the TSM, the dominant signal component, if present, can be subject to shadowing. To support the use of the TSM, we develop novel formulations for the probability density functions of the in-phase and quadrature components of the complex received signal as well of the received signal envelope. The offered results are corroborated with results from respective computer simulations, whilst it is shown that the proposed model is more versatile than existing conventional models.