<![CDATA[The digital transformation in education requires an adaptive and innovative pedagogical architecture to effectively manage teaching methods. Traditional learning systems often fail to adjust instructional strategies to individual learners’ needs. Therefore, this study developed the Adaptive Innovative Pedagogical Architecture (AIPA–SIM), a multi-agent simulation model designed to analyze the dynamics of adaptive teaching method management. This study employed a simulation-based experimental design with three learning system scenarios: non-adaptive, semi-adaptive, and fully adaptive. The model was developed using NetLogo 6.3.0 and Python Mesa Framework, incorporating teacher, student, and system agents. Key parameters included Learning Performance, Adaptivity Rate, Engagement Index, and System Efficiency. Data were analyzed using ANOVA and Tukey HSD tests to compare system performance. Simulation results revealed that the fully adaptive system (AIPA–SIM) significantly improved learning performance (+29.5%) compared to the non-adaptive system (p < 0.01). The Engagement Index reached 0.92 and System Efficiency achieved 93.7, indicating high responsiveness to students’ behavioral changes. A strong positive correlation (r = 0.88) was observed between adaptivity level and learning performance. The AIPA–SIM model proved effective as an adaptive pedagogical architecture capable of enhancing learning outcomes through automated, agent-based decision-making mechanisms. This research provides both conceptual and practical contributions to the development of smart adaptive learning systems and data-driven educational policies in the digital era.]]>
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