Adaptive Cognitive Pathway Network and Knowledge-Potential Optimization for Personalized Learning

The rapid advancement of generative AI and large-scale educational models has opened new avenues for enhancing personalized learning systems. While adaptive learning technologies have seen growing adoption, many existing methods rely on static representations of learners, simplistic sequencing of content, and insufficient modeling of dynamic cognitive states. These limitations hinder long-term knowledge acquisition, engagement, and the development of sustainable, individualized learning trajectories. Moreover, traditional frameworks often overlook the complex interdependencies among learning resources and fail to align content delivery with learners’ evolving needs and cognitive profiles. To overcome these challenges, this study proposes a unified framework that integrates fine-grained learner modeling, dynamic content sequencing, and adaptive pathway optimization. At its core is the Adaptive Cognitive Pathway Network (ACPN), a deep sequence modeling architecture that captures evolving knowledge states through hierarchical representations of educational resources. Complementing this, we introduce the Knowledge Potential Guided Optimization (KPGO) strategy, which incorporates cognitive constraints, engagement dynamics, and long-term performance indicators to personalize learning trajectories more effectively. Through extensive experimental evaluations, the proposed framework demonstrates substantial improvements in recommendation accuracy, knowledge retention, and learner engagement, outperforming baseline adaptive systems across various educational scenarios. The results confirm the scalability and robustness of the approach in supporting individualized, long-term learning processes. This work contributes a forward-looking solution for intelligent learning systems by bridging generative modeling, cognitive theory, and sequential decision-making — advancing the next generation of personalized education technologies.