Modeling Lamastum Parameter–Variable Systems Using Lagrange Deep Learning Methodologies for Education and Life Sciences

Abstrak

The significant progress of Artificial Intelligence (AI), particularly within Deep Learning paradigms, has enabled the exploration of new frameworks for representing language as symbolic and semantic systems. Contemporary AI is no longer confined to numerical computation but increasingly addresses the complexity of meaning embedded in linguistic structures..One of the main challenges in educational AI is how to model non-numerical parameters and variables—such as language, conceptual meaning, and ethical values—within mathematical systems that remain computationally optimizable. This study proposes modeling the word “Lamastum” as a system of semantic parameters and variables using a Lagrange Deep Learning approach. The Lagrange method is employed to link learning objective functions with constraints related to values, ethics, and life contexts through constrained optimization formulations . The Lagrangian approach enables simultaneous integration of learning objectives and humanistic. The results indicate that this approach can represent interactions among linguistic meaning, educational goals, and real-life contexts in a more structured and adaptive manner. The proposed model has the potential to serve as a new conceptual framework for the development of humanistic AI oriented toward sustainable education and character formation Originally developed for constrained mathematical optimization, the Lagrangian approach has been increasingly adopted in contemporary AI research to integrate human-centered constraints into machine learning systems.