Dynamic model inspired by artificial intelligence for engineering education and complex systems design: a nonlinear differential systems approach
DOI:
https://doi.org/10.58951/dataset.2026.001Keywords:
Artificial Intelligence, Mathematical Modeling, Engineering Education, Dynamic Systems, Adaptive Learning, Intelligent Educational SystemsAbstract
The growing integration of Artificial Intelligence (AI) into higher education has stimulated the development of innovative solutions to enhance teaching and learning processes; however, a significant gap remains in the formulation of mathematical models capable of explicitly representing the dynamics among learning, academic performance, and intelligent technological support, particularly in engineering education. This study proposes a nonlinear dynamic mathematical model inspired by AI-based adaptive mechanisms to analyze the interactions among student knowledge, teaching efficiency, engineering performance, and the use of intelligent learning support resources. The research adopted a quantitative approach based on mathematical modeling and computational simulation, using a system of ordinary differential equations implemented in the R programming language. Numerical integration was performed using the fourth-order Runge-Kutta method, and a sensitivity analysis of the main model parameters was conducted. The simulation results indicate that AI-inspired adaptive mechanisms promote increased knowledge acquisition, improved pedagogical efficiency, and sustained growth in students’ technical performance. The adaptive scenario supported by intelligent resources outperformed the traditional scenario, demonstrating faster and more sustainable learning trajectories. Furthermore, the sensitivity analysis confirmed the structural robustness of the proposed model under small parameter perturbations. It is concluded that dynamic modeling represents a promising framework for understanding and optimizing complex educational systems, providing a theoretical foundation for future empirical validation studies and the development of intelligent engineering education environments.
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