Hybrid Learning Ecosystems in Post-Conflict: Empirical Integration of Cognitive Progression and Psychosocial variables toward the Mathprogia framework

Abstract

This article presents the results of an interdisciplinary empirical study conducted in educational settings affected by armed conflict, aimed at analyzing hybrid learning ecosystems as mechanisms for transforming disrupted educational trajectories. The study systematizes accumulated evidence from a sustained research program, initially focused on the evolution of mental models in digital environments and subsequently expanded to include psychosocial variables associated with educational retention and academic performance.

Using a mixed-methods and longitudinal approach, Knowledge Space Theory (KST) and Structural Equation Modeling (SEM) are integrated to examine cognitive progression and latent constructs such as educational resilience, academic self-efficacy, academic anxiety, and life-project projection. The evidence indicates that adaptive personalization enhances the stability of educational trajectories and conceptual progression, while reducing adverse psychosocial factors when articulated with social dialogue methodologies and context-sensitive pedagogical practices. 

Based on this systematization, the MathProgIA framework is proposed as an integrative conceptual and methodological hypothesis aimed at articulating adaptive personalization, mental model analysis, and principles of explainable artificial intelligence within hybrid learning ecosystems. Although MathProgIA has not yet been empirically validated, it is presented as an empirically informed proposal that organizes and interprets the accumulated evidence and defines a future agenda for experimental validation.

It is concluded that hybrid ecosystems, designed from an interdisciplinary and context-sensitive perspective, constitute a solid foundation for developing integrative frameworks aimed at promoting educational equity in post-conflict scenarios.