Toward Inclusive and Interdisciplinary Applied Mathematics in the Digital Age
Keywords:
Applied Mathematics, Mathematical Modeling, STEM Education, Machine Learning, Interdisciplinary Collaboration, Educational Innovation, Neural NetworksAbstract
Applied mathematics has become increasingly essential in solving real-world problems across engineering and the natural sciences. This narrative review aims to synthesize recent literature on modeling techniques, AI integration, and pedagogical innovations in applied mathematics. Using Scopus, Web of Science, and PubMed, the study employed a structured keyword-based search strategy to identify peer-reviewed research from 2013 to 2024. Inclusion criteria focused on interdisciplinary applications of applied mathematics, original research, and evidence-based pedagogical insights. The review reveals that mathematical modeling, especially when combined with machine learning and physics-informed neural networks, significantly enhances computational accuracy and efficiency. Deep learning approaches to partial differential equations and optimization algorithms like Shehu duality or metaheuristics are proving transformative in dynamic system simulations. However, disparities in access, conceptual understanding among students, and a lack of inclusive educational practices limit the full potential of these innovations. Sociocultural and economic factors strongly influence the implementation of mathematical technologies across regions. The findings suggest that systemic curriculum reform, investment in teacher capacity, and stronger collaboration between academia and industry are key to closing these gaps. Future research should prioritize longitudinal evaluations and culturally responsive approaches. By aligning pedagogical strategies with technological progress, applied mathematics can serve as a pivotal force in advancing global scientific and engineering challenges.
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