We ran an action research on content co-creation for 53 novice programming students at Master level from diverse backgrounds, aiming to increase motivation and understanding. An optional survey gathered positive feedback from all 26 respondents regarding their motivation and understanding. These findings highlight the positive impact of inclusive approaches in programming education, warranting further exploration in larger settings.

This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets and aimed to differentiate between COVID-19, non-COVID-19, and healthy cases. MSTAC employs two classification stages: the first distinguishes healthy from unhealthy cases, and the second further classifies COVID-19 and non-COVID-19 cases. Compared to a single CNN-Multiclass model, MSTAC demonstrated superior classification performance, achieving 97.30% accuracy and sensitivity. In contrast, the CNN-Multiclass model showed 94.76% accuracy and sensitivity. MSTAC’s effectiveness is highlighted in its promising results over the CNN-Multiclass model, suggesting its potential to assist healthcare professionals in efficiently diagnosing COVID-19 cases. The system outperformed similar techniques, emphasizing its accuracy and efficiency in COVID-19 diagnosis. This research underscores MSTAC as a valuable tool in medical image analysis for enhanced disease classification.

The adoption of artificial intelligence (AI) in cardiovascular disease prediction has significantly improved risk stratification, offering new avenues for early diagnosis and preventive care. With the growing availability of electronic health records and structured clinical datasets, machine learning (ML) and deep learning (DL) models have demonstrated strong predictive capabilities. However, despite their performance, its adoption in healthcare is often constrained by the lack of transparency and interpretability in many ML and DL models. This lack of explainability undermines clinical trust and raises ethical concerns. In high-stakes domains such as CVD prediction, clinicians require not only accurate outputs but also clear explanations of how those predictions are derived. This paper presents a comparative evaluation of explainable artificial intelligence (XAI) techniques applied to both conventional ML models such as Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest and DL architectures including AutoInt, FT-Transformer, and Category Embedding. Using the Framingham Heart Study dataset, this study integrates SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to assess model interpretability and feature relevance. Results show that conventional models offer superior explainability with comparable predictive accuracy, while DL models, although slightly less interpretable, demonstrate potential with advanced XAI techniques. The findings advocate hybrid approaches that balance accuracy and interpretability, supporting ethical and practical AI deployment in healthcare.

We conducted action research on a programming class of 66 MSc students following a content co-creation approach. This allowed students to create problems and write programs to solve them, enhancing their understanding and programming skills. Pre-tests, post-tests, and a survey assessed their knowledge and opinions. Post-test scores on 28 consenting participants showed significant improvement and enhanced comprehension irrespective of their background. Positive feedback highlighted improvements in critical thinking, motivation, and a sense of belonging. The results evidence a positive impact of content co-creation, proving it to be an inclusive approach to develop all students’ coding skills.