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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63279
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Engineering
Sinhgad Academy of Engineering, Pune
Computer Engineering
Sinhgad Academy of Engineering, Pune
Computer Engineering
Sinhgad Academy of Engineering, Pune
Computer Engineering
Sinhgad Academy of Engineering, Pune
Computer Engineering
Sinhgad Academy of Engineering, Pune
Traditional study planning and scheduling systems are mostly static and cannot meet the changing academic requirements, learning speed and students' performance. In this paper, an AI-Based Dynamic Study Management System is presented in order to facilitate the efficient learning process and academic productivity using intelligent scheduling, NLP-based text summarization and adaptive quiz generation. The proposed system follows the concept of study topics as schedulable tasks and dynamically assigns them in available time slots according to its duration, priority, deadlines and user performance. AI models are used for automated content summarization, and adaptive quizzes are created with AI-driven prompt engineering techniques. The architecture can be scaled and is responsive, using React, FastAPI, TensorFlow, and MongoDB. The system also applies ideas of reinforcement learning in order to be able to reschedule adaptively and to provide personalized study recommendations. The proposed implementation is analyzed and compared with contemporary AI systems, pinpointing its strengths and weaknesses in terms of speed, privacy, adaptability, and computational efficiency. This proposed solution is designed to ease the burden, increase the retention rate, and offer an intelligent solution to traditional solutions for planning study.
KEYWORDS
This version of the AI-Based Dynamic Study Management System provides an intelligent, adaptive way to schedule, summarize, and quiz, thereby making static planning an intelligent, adaptive tool. It fills in the missing pieces for current tools in use to increase efficiency and retention, and decrease stress, as proven in experimental setups. Enhancements to come may include adding learning management systems, predictive analytics, and voice assistants for added accessibility.
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Publication Date
5/15/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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