From Predictors to Mechanisms: Interpretable Artificial Intelligence Evidence on Mathematics Achievement and Cognitive Learning Systems

Understanding academic achievement requires moving beyond the identification of influential factors toward explaining how these factors are organized into functional learning and cognitive mechanisms. Although prior research has extensively documented the roles of socioeconomic status, student attitudes, and learning behaviors, less attention has been paid to how these elements interact within structured pathways that reflect underlying learning intelligence across educational systems. This study adopts a mechanism-oriented perspective to examine mathematics achievement using data from PISA 2018. Focusing on high-performing regions in East Asia and Western countries, it integrates interpretable artificial intelligence methods with structural modeling to investigate how contextual, psychological, and learning-process factors jointly shape achievement outcomes. The findings show that high achievement is not governed by a single set of dominant predictors, but by distinct organizational mechanisms of learning intelligence. In East Asian systems, achievement follows a chain-like convergent structure, in which socioeconomic background is systematically translated into academic outcomes through sequential psychological and self-regulatory processes. Psychological factors, particularly educational expectations and self-beliefs, function as key mediating mechanisms that organize learning engagement and strategy use. By contrast, high-performing systems in Europe and North America exhibit a parallel configuration, in which multiple cognitive and behavioral factors independently contribute to achievement through more decentralized pathways, reflecting a distributed structure of learning intelligence. Across regions, learning processes such as reading engagement and digital literacy show consistently positive associations with achievement. However, their roles vary depending on how they are embedded within broader system-level structures. These results suggest that self-regulation operates not merely as an associated factor, but as an organizing mechanism of learning intelligence that structures the translation of background resources into performance. By reconceptualizing prediction as a means of revealing the organization of learning intelligence, this study proposes a unified analytical framework that links interpretable artificial intelligence with theory-driven explanation. The findings contribute to a deeper understanding of how achievement systems function and highlight that high performance can emerge through multiple, structurally distinct pathways, with important implications for educational research, cognitive theory, and policy design.