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 describes the design and implementation of an automated irrigation control system that uses data collected by a wireless sensor network. Each sensor node, built on a custom-designed printed circuit board, includes sensors for light intensity, temperature, and a custom soil moisture sensor. Data is transmitted to a central control node via ESP-NOW, where it is processed and compared with configurable thresholds retrieved from Google Sheets over Wi-Fi. Irrigation is triggered automatically when conditions meet the remotely defined thresholds. A key contribution is the development and testing of a custom soil moisture sensor, with results compared to commercial models. The system supports low-power operation through deep sleep modes, enabling long-term field deployment. The novelty lies in the complete integration of hardware, software, and cloud-based control, providing a flexible and low-cost solution for precision agriculture. The system can be deployed in greenhouses or open fields and serves as a platform for future research in smart irrigation. The fundamental aspect is a very user-friendly solution for any farmer attributable to easy accommodation to the Google Sheets interface, no maintenance cost over the cloud account, and up to 45 days of battery life or a built-in alternative for solar power.