The integration of Artificial Intelligence (AI) across various fields has transformed the educational landscape and demands a targeted approach to teaching AI in an academic setting. As lecturers aim to prepare students for an AI-driven future, they face various challenges arising from the complex and mathematical nature of AI. This paper explores the challenges of teaching and assessing AI modules in large classrooms by implementing a student-centred approach alongside formative assessment and feedback. It also examines issues related to the diversity of students' skill sets and learning style. This study was conducted on two different cohorts of the same module, Video Analytics and Machine Learning during 2022-2024. Two distinct cohorts were chosen to ensure unbiased conclusions in our study. By recognising and actively addressing these challenges, lecturers can more effectively equip students with the skills needed to navigate this rapidly evolving field. In conclusion, this study shows that implementing formative assessments like quizzes and student-centred approaches are highly beneficial in large classrooms and lead to a significant improvement in student performance and learning outcomes. In addition, the analysis shows that students who are more actively engaged with quizzes tend to score higher on the module. While the overall student feedback has been positive and there has been noticeable improvement in performance, it is important to recognise that there have been instances of unsatisfactory student outcomes as well.

User Experience (UX) Design, while not a new term, has become significantly importance in recent years. It includes how users interact with software, focusing not only on completing specific tasks efficiently and error-free, but also on the overall experience. This contains the emotional impact, user satisfaction, and whether the experience was enjoyable and worth recommending to others. However, teaching these concepts within a module in an academic setting presents complications. Students often lack real-world experience that making it harder for them to appreciate how deeply these factors influence user behaviours and product success. In our case study, students registering in this module often come from different cohorts, such as Business Information Technology (BIT) and Computing and Information Technology (CIT). While BIT and CIT pathways both integrate aspects of technology, they serve different educational and career purposes. Teaching UX to these students can be challenging, potentially impacting their performance in the final project-based assignment. These issues can be categorised into theoretical and practical aspects that can be addressed by considering students different learning styles and create a more inclusive and effective learning environment. This paper explores the student-centred approach particularly within the framework of a User Experience Design module. The study was carried out over two academic years of the same module. In the first cohort, only slight modifications were introduced, while the second cohort fully embraced the proposed techniques. The results revealed a significant improvement in the students' final project performance after the full implementation of this approach.

Computer science including data analytics is a widely popular field, boasting promising career opportunities in the future. Proficiency in programming stands as a fundamental requirement for success in this domain. However, students entering MSc programs in data analytics often possess varying levels of programming background, which can impact their performance in assignments. Recognising and addressing these differences through tailored instruction can improve students’ outcomes. This paper explores the importance of considering students' programming backgrounds in the data analytics field and highlights strategies to enhance their performance based on prior knowledge. This study was carried out on two different modules in two different pathways. We have chosen two distinct cohorts and pathways to ensure unbiased conclusions in our study. The initial research was applied to the Database and Programming Fundamentals module for an MSc data analytics cohort, and then we utilized a Deep Learning module for final year computer science undergraduates as a validation cohort. As a conclusion, this study successfully demonstrated a significant increase in student assignment performance through the implementation of tailored instruction based on students' programming backgrounds. Despite receiving positive student feedback and observing excellent and improved performances, it is crucial to acknowledge instances of unsatisfactory student performance as well. Both studies were conducted by the School of Electronics, Electrical Engineering, and Computer Science (EEECS) at Queen's University Belfast (QUB) during the academic year 2021/2022.

BACKGROUND: Transcriptionally informed predictions are increasingly important for sub-typing cancer patients, understanding underlying biology and to inform novel treatment strategies. For instance, colorectal cancers (CRCs) can be classified into four CRC consensus molecular subgroups (CMS) or five intrinsic (CRIS) sub-types that have prognostic and predictive value. Breast cancer (BRCA) has five PAM50 molecular subgroups with similar value, and the OncotypeDX test provides transcriptomic based clinically actionable treatment-risk stratification. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time consuming and requires significant bioinformatics experience. There is no "universal" method of using data from diverse assay/sequencing platforms to provide subgroup classification using the established classifier sets of genes (CMS, CRIS, PAM50, OncotypeDX), nor one which in provides additional useful functional annotations such as cellular composition, single-sample Gene Set Enrichment Analysis, or prediction of transcription factor activity. RESULTS: To address this bottleneck, we developed classifieR, an easy-to-use R-Shiny based web application that supports flexible rapid single sample annotation of transcriptional profiles derived from cancer patient samples form diverse platforms. We demonstrate the utility of the " classifieR" framework to applications focused on the analysis of transcriptional profiles from colorectal (classifieRc) and breast (classifieRb). Samples are annotated with disease relevant transcriptional subgroups (CMS/CRIS sub-types in classifieRc and PAM50/inferred OncotypeDX in classifieRb), estimation of cellular composition using MCP-counter and xCell, single-sample Gene Set Enrichment Analysis (ssGSEA) and transcription factor activity predictions with Discriminant Regulon Expression Analysis (DoRothEA). CONCLUSIONS: classifieR provides a framework which enables labs without access to a dedicated bioinformation can get information on the molecular makeup of their samples, providing an insight into patient prognosis, druggability and also as a tool for analysis and discovery. Applications are hosted online at https://generatr.qub.ac.uk/app/classifieRc and https://generatr.qub.ac.uk/app/classifieRb after signing up for an account on https://generatr.qub.ac.uk .