Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Call for Paper Volume 7, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
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Volume 7 Issue 2
March-April 2026

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AI Powered Hybrid Optimizer Learning for Students

Author(s) Mr Yalamanchili Venkata Sai Sasank, K. PRAGASH, LOKESH P, PREM M, VASANNTH S
Country India
Abstract Student learning systems require efficient management of online and offline learning experiences and computational resources. For utilizing the services of the system, there is no need for separate management of multiple learning platforms or manual tracking of performance data. Using simple internet connectivity, anyone can access the personalized learning services. In addition to that, it offers secure storage, tracking, and analysis of student performance and learning progress. Therefore, the accuracy and reliability of the mapped student data is a primary and essential concern in hybrid learning systems. Thus, the data security of the stored student information is essentially strong and access through private accounts is also secured. But the data transmission between teachers, students, and parents is performed over standard networks which may not always be trusted. Therefore, a secure data transfer and access service is required to design. On the other hand, students often study across multiple subjects and modes, but their learning schedules and resources are not optimized effectively. Thus, a personalized learning schedule and recommendation system is desired to implement for students. This paper gives an overview of the state of hybrid learning management systems and their AI-driven techniques for performance tracking and personalized resource recommendation. Additionally, for specification of the paper, we suggest our solution along with problem identification
Keywords Hybrid Learning, Personalized Learning, AI-driven LMS, Student Performance Analysis, Recommendation System, Study Schedule Optimization, Adaptive Assessment
Field Engineering
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-29
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E-ISSN 3048-7641
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