ABSTRACT: Nowadays, the growing intersection between artificial intelligence (AI) models and its usage within education, has paved the way for innovative approaches to assess and improve engineering education initiatives, particularly those that rely on STEAM epistemology principles and, therefore, based on the core elements of Computational Thinking (CT). Projects aligned with CT goals, utilize a problem – based solving methodology, inspired by computer science concepts. This approach is not limited to coding, but applied to tackling complex open engineering problems, across various disciplines, including science, technology, engineering and mathematics, using strategies that are suitable for automation or computational modeling. A well-known framework, applicable within STEAM projects, which consists of a series of steps that students follow, in order to design a prototype artifact and find a solution to a complex problem is the Engineering Design Process (EDP). This paper investigates the impact of AI based methods and tools (i.e. GenAI tools) on STEAM engineering literacy among University students, especially within the content of next generation digital systems,  sensors and low power devices for precision agriculture application domain. Utilizing a rubric – based assessment and applying EDP process, the study evaluates two student teams tasked to design and implement a smart greenhouse, equipped with various sensors, actuators, automation and digital systems and data driven analytics capabilities. In particular, team A completed the project without using GenAI assistance, while team B employed GenAI tools throughout their design and implantation process. Comparative analysis of rubric based outcomes, indicates that GenAI assisted team demonstrates superior performance across all key STEAM engineering literacy dimensions, including analytical thinking, innovation and practical application of digital systems. Additionally using a pre and post - test design, the study measures knowledge acquisition related to digital automation systems, alongside student engagement, confidence in learning and AI tool effectiveness. Post - test results demonstrate a significant improvement in STEAM literacy, as well as positive shifts in engagement and confidence. Overall, our findings underscore the potential of GenAI, to significantly enhance students’ ability to tackle complex, semi – defined engineering problems, highlighting its relevance for modern engineering education curricula.

ABSTRACT: Over the last three decades, discussions, rhetoric, recommendations have proposed numerous suggestions for the  integration of AI in Education (AI Ed). Research on AI Ed focuses on its goals, how it is applied in teaching and learning sequences, how it is perceived by teachers and students as well as by policy makers. Other issues closely related to AI Ed are: a) the difficulty in defining AI due to the interdisciplinary nature of the field, b) the possibility of AI Ed for learning that is more adaptive, inclusive, and engaging, c) the effective encompassment of assessment, d) the predictive tools AI Ed provides, e)  the relation of TPACK with generative AI, f) the epistemology of STEM with the impact of AI, and also g) ethics considerations.