Abstract:

Understanding complex systems is increasingly important in STEM education for addressing contemporary scientific, environmental, and societal challenges. Within the STEAMSMART educational framework, currently implemented with gifted secondary school students, participants engage in hands-on exploration of complex systems through computational modeling, simulation, environmental sensing and data acquisition. The initiative is currently expanding from a local and regional educational program toward a broader national framework, developed in collaboration with the Italian Association for Physics Teaching (AIF) and with the interest and support of the University of Genoa, particularly the Department of Physics (DIFI) and potentially other departments such as DIBRIS.

Students develop and experiment with a variety of computational artifacts inspired by physics and complex systems science, including Daisyworld simulations, artificial life models, Monte Carlo methods, fractal generation, and dynamic models of chaotic systems. These activities allow learners to explore key concepts such as feedback mechanisms, emergent behavior, nonlinear dynamics, and probabilistic processes.

To connect simulations with real-world phenomena, environmental data are collected using IoT sensor platforms based on ESP32, Arduino microcontrollers, and micro:bit devices, enabling the acquisition of parameters such as temperature, light intensity, humidity, and other environmental variables. These data streams can be used both for analysis and as inputs for machine learning experiments, neural network models, and generative AI-assisted exploration, allowing students to approach data-driven modeling, prediction, and pattern discovery.

Programming and experimentation activities are carried out using collaborative coding practices, including emerging approaches such as “vibe coding”, in coevolution with locally deployed AI systems that support exploration, debugging, and iterative model development.

By combining computational simulations, IoT-based environmental sensing, and AI-assisted data analysis, this approach promotes active and experiential learning while providing students with an integrated understanding of complex systems across physics, mathematics, computation, and environmental science. The STEAMSMART framework aims to provide a replicable and scalable educational model for introducing interdisciplinary STEM and AI learning experiences centered on complex systems, environmental data analysis, and real-world problem solving.

Keywords:

Complex Systems, STEM, Physics, Artificial Intelligence, Internet of Things, Machine Learning, Environmental Data Analysis, Experiential Learning.