AI-Based Models for Assessing STEAM Engineering Literacy for University Students: The Case of Digital Systems for Precision Agriculture
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https://doi.org/10.51724/hjstemed.v4i1.37Keywords:
STEAM literacy, Digital Systems, Engineering activities, GenAI tools, computational thinking, Automation, Sensors, Engineering Design ProcessAbstract
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.
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