This paper investigates the impact of self-revision quizzes on student engagement and reflective learning in a Transnational Education (TNE) programme module. Designed around Kolb's Experiential Learning Cycle, the quizzes em-phasise four stages: concrete experience, reflective observation, abstract conceptualisation, and active experimentation, encour-aging students to identify knowledge gaps and apply feedback iteratively. Reflective learning supports metacognition and self-assessment, helping students enhance engagement and deepen their understanding of complex topics. Introduced in 2020/21, the self-revision quizzes provided immediate feedback with brief validation for correct answers and detailed explanations for incorrect ones, guiding students back to relevant teaching materials. Questions were based on recurring queries in QMPlus (Queen Mary's Virtual Learning Environment) and in-class discussions, targeting challenging areas of the module. Designed as formative assessments, the quizzes allowed multiple attempts to promote continuous revision. Over four years (2020/21 to 2023/24), quiz timing and reminders were adjusted to maximise participation. Results show that engagement varied between 25% and 57% per year, with the highest engagement linked to well-timed quizzes before assessments and multiple reminders. Feedback from the 2023/24 cohort revealed 55% of respondents found the quizzes very helpful for clarifying concepts, while 39% found them somewhat helpful but acknowledged the need for additional practice. Moreover, students who engaged with the quizzes consistently performed better in both final exams and the coursework. This study highlights the potential of self-revision quizzes to enhance engagement and prepare students for assessments such as exams, particularly in TNE contexts. It contributes to formative assessment research by showcasing how reflective learning tools can drive continuous learning. Plans are underway to integrate Generative AI for tailored feedback and quiz automation, reducing academic workload and expanding applicability to other modules.

The effective use of Generative Artificial Intelligence (GenAI) in education is rapidly increasing at all levels and educators are exploring this technology to make the best use of it. This paper proposes exploiting GenAI to help engineering students perform their laboratory tasks effectively. In Transnational Education (TNE) programmes based on block teaching, students may face uneven intensive study load with nonconsistent face-to-face student-teacher contact throughout the term. There is a post-COVID impact even after lifting restrictions and bringing a change in students' behaviour of more reliance on online resources rather than in-class lectures. To address these concerns, this work aims to develop a GenAI-based tool that leverages student reliance on self-study while filling the gap between teacher-student interaction during non-teaching weeks. The AI mentor will be informed/trained with all the lab-related material for the module and is expected to support students during the lab sessions. In this paper, the authors present a pilot study and engage third-year engineering students to evaluate their willingness to use existing GenAI based code assistants in solving lab tasks that involve programming exercises. Results indicate students use GenAI tools either occasionally or frequently, to assist with programming-related assignments. When applied to lab tasks, majority of the students find GenAI code assistant quite helpful for understanding concepts and solving the programming tasks. Additionally, students utilize these tools for diverse purposes, including writing code, solving errors, and answering questions. Findings reveal high enthusiasm among students for incorporating GenAI based code assistants into labs. In the future, this work aims to do refinements in further studies and engage second-year engineering students as we believe engaging them will provide a broader perspective on the adoption and use of these technologies. As engineering graduates are generally expected to swiftly adapt to new technologies and digital environments, developing this ability pre-graduation is crucial for their skillset and their professional readiness.

This paper presents an AI-assisted approach that leverages Multimodal Large Language Models (MLLMs) to automate the generation of Multiple-Choice Questions (MCQs) for modules in engineering education. The system introduces a LOs extraction to MCQs generation pipeline, which extracts Learning Outcomes (LOs) from provided lecture notes and generates relevant MCQs with solutions and explanations based on the extracted LOs. By harnessing MLLMs' capabilities in vision and text comprehension, coupled with carefully crafted prompts from human educators, the tool efficiently produces context-relevant MCQs that can streamline teaching material development. The effectiveness of this AI-powered MCQ generation pipeline is investigated through experiments across a number of engineering modules with evaluations on the quality of the generated MCQs by human educators. The analysis of the evaluation results shows the AI tool's ability to generate MCQs that are well-aligned with LOs and exhibit strong contextual relevance, demonstrating the potential of AI-assisted approaches to enhance the efficiency of creating high-quality MCQs in engineering education. However, the variability in quality ratings across different aspects underscores the continued need for human expertise and oversight in the assessment design process. The findings provide useful insights into the capabilities and limitations of state-of-the-art multimodal language models in supporting assessment development in engineering education.

Nowadays, although the performance of network devices has improved a lot, they consume more and more energy. Therefore, network energy efficiency is increasingly important in this era. In this paper, a software approach to energy-efficient networks is proposed and an application, which can make mid-scale networks energy efficient is demonstrated. In the proposed approach, some idle network interfaces of routers are disabled when the network load is low. Traffic flow converges on certain network paths, as disabled interfaces make some paths unavailable. When network load gets high, disabled interfaces can be re-enabled to ease the traffic pressure and network traffic disperses to different paths. A dynamic routing protocol is configured in order to keep connectivity when the topology changes. This approach is realized by an application, which is developed using Juniper Junos Software Development Kit (Junos SDK) and operates on Juniper programmable routers. Multiple entities of the application operating on different routers in the network form a distributed system. With regards to functionality, the application senses network traffic load; communicates among entities on other routers; determines and executes interface disabling and enabling. A virtual network built by Juniper virtual network simulator is used to test this application. Results reveal that a reasonable amount of energy can be saved by disabling idle interfaces while maintaining the connectivity.