Increasing disruption from Industry 4.0/5.0, climate change, digital transformation, wars and high levels of volatility, uncertainty, complexity and ambiguity (VUCA) are leading to major changes in the labor market. In addition, improvements in chatbots and Generative AI are opening new opportunities for students and the future workforce. They seem to have become ‘creative’ in their field of study and are turning to AI applications for their learning, assignments or theses. However, there are concerns about academic integrity and cybersecurity. Students and researchers have already started using AI chatbots to learn, program, write academic essays or conduct research faster and more effectively. This paper explores whether AI can serve as a bicycle for human creativity and innovation. What are the first words that come to mind when you think of Engineering Education 5.0? Have you used Artificial Intelligence tools in your teaching, learning or research? This paper presents the results of a survey conducted at the Faculty of Electronics, Technology and Information Technology on the challenges and opportunities of using Generative AI(GenAI) in Engineering Education. This paper also outlines the Generative AI ecosystem for teaching, learning and research. Engineering Education 5.0 needs a future ready Curricula including AI Literacy, new teaching and learning methods, analytical and critical thinking, reflection, collaboration and complex problem-solving, and communities of practice with the industry to foster innovation and creativity.

This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. As Machine Learning algorithms become increasingly embedded in decision-making processes, particularly for traffic management and other high-level commitment applications, concerns regarding the transparency and trustworthiness of complex ’black-box’ models have grown. Theoretical assertions often propose a trade-off between model complexity (predictive performance) and transparency (interpretability); however, empirical evidence supporting this claim is limited and inconsistent. To address this gap, we introduce a novel interpretability scoring system - a Machine Learning Interpretability Rank-based scale - that combines objective measures such as the number of model parameters with subjective interpretability rankings across different model types. This comprehensive methodology includes stratified sampling, model tuning, and a two-step ranking system to operationalize this trade-off. Results reveal a significant negative correlation between interpretability and predictive performance for intrinsically interpretable models, reinforcing the notion of a trade-off. However, this relationship does not hold for black-box models, suggesting that for these algorithms, predictive performance can be prioritized over interpretability. This study contributes to the ongoing discourse on explainable AI, providing practical insights and tools to help researchers and practitioners achieve a balance between model complexity and transparency. We recommend to prioritise more interpretable models when predictive performance is comparable. Our scale provides a transparent and efficient framework for implementing this heuristic and improving parameter optimization. Further research should extend this analysis to unstructured data, explore different interpretability methods, and develop new metrics for evaluating the trade-off across diverse contexts.