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

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Τίτλος: Adaptive Cognitive Pathway Network and Knowledge-Potential Optimization for Personalized Learning.
Συγγραφείς: Hu, Hai1 (AUTHOR) 18662770526@163.com, Wang, Aiguo1 (AUTHOR), Zhu, Yunan1 (AUTHOR)
Πηγή: International Journal of High Speed Electronics & Systems. Dec2026, Vol. 35 Issue 6, p1-28. 28p.
Θεματικοί όροι: *Generative artificial intelligence, *Decision making, Individualized instruction, Student engagement, Mental representation, Educational technology, Memory, Instructional systems
Περίληψη: 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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Adaptive Cognitive Pathway Network and Knowledge-Potential Optimization for Personalized Learning.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1142/S0129156425408058
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      – Code: eng
        Text: English
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        PageCount: 28
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      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Individualized instruction
        Type: general
      – SubjectFull: Student engagement
        Type: general
      – SubjectFull: Mental representation
        Type: general
      – SubjectFull: Educational technology
        Type: general
      – SubjectFull: Memory
        Type: general
      – SubjectFull: Instructional systems
        Type: general
    Titles:
      – TitleFull: Adaptive Cognitive Pathway Network and Knowledge-Potential Optimization for Personalized Learning.
        Type: main
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          Name:
            NameFull: Hu, Hai
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            NameFull: Wang, Aiguo
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            NameFull: Zhu, Yunan
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          Dates:
            – D: 01
              M: 12
              Text: Dec2026
              Type: published
              Y: 2026
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              Value: 35
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            – TitleFull: International Journal of High Speed Electronics & Systems
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