AI Powered Smart Study Planner

Authors: Shreyas Yerole, Shubham Rajhans, Tejas Shelar, Aditya Shirke, Akash Sasave

DOI Link: https://doi.org/10.22214/ijraset.2026.78794

Abstract

Artificial Intelligence (AI) is transforming many sectors, including education and learning systems. AI technologies such as machine learning, intelligent recommendation systems, and automated analytics are helping students improve study efficiency and academic performance. However, many students still struggle with time management, subject prioritization, and maintaining consistent study schedules. This research paper proposes an AI Smart Study Planner System that automatically generates personalized study schedules, identifies weak subjects, and provides AI-generated quizzes to improve learning outcomes. The system integrates modern web technologies with AI models to analyze student performance and recommend optimized study plans. The proposed system helps students organize their study routines, track progress, and receive intelligent recommendations. The results indicate that AI-based study planning systems can significantly improve productivity, reduce manual planning effort, and support students in achieving better academic performance.

Introduction

The AI Smart Study Planner is an intelligent educational tool designed to help students manage study schedules, track performance, and improve learning outcomes. By leveraging Artificial Intelligence and machine learning, the system analyzes student data—such as subjects, exam dates, available study hours, and quiz results—to generate personalized study plans, adaptive quizzes, and revision strategies. It features a layered architecture: a data input layer for collecting information, a processing layer for AI-based analysis, an application layer for interactive tools like progress tracking and reminders, and an output layer for displaying schedules, reports, and quizzes. The platform is implemented with React.js frontend, Django REST backend, and AI services via Google Generative AI (Gemini API), ensuring a responsive, secure, and modular system. Overall, it provides a structured, adaptive, and user-friendly solution to enhance time management, study efficiency, and academic performance.

Conclusion

The AI Smart Study Planner system demonstrates how Artificial Intelligence can be effectively applied in the field of education to improve study management and learning efficiency. Many students face difficulties in organizing their study schedules, prioritizing subjects, and maintaining consistent study habits. Traditional methods such as handwritten timetables or basic planners often lack flexibility and cannot adapt to changing exam schedules or individual learning needs. The proposed system addresses these challenges by introducing an intelligent platform that automatically generates personalized study plans and supports students in managing their academic activities more effectively. The system utilizes AI techniques to analyze student inputs such as subjects, exam dates, daily study hours, and quiz performance. Based on this data, the system generates optimized study schedules that help students allocate their time efficiently and focus on subjects that require more attention. The inclusion of AI-generated quizzes allows students to test their knowledge and identify areas that need improvement. Additionally, the progress tracking feature helps students monitor their learning performance and maintain consistent study routines. The integration of modern web technologies further enhances the usability of the system by providing a simple and interactive interface for students. Through features such as automated timetable generation, performance analytics, and study reminders, the system supports a structured learning approach that improves productivity and exam preparation. The intelligent recommendations provided by the system also help students make better decisions regarding their study strategies.

References

[1] Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson Education. [2] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. [3] Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann. [4] Baker, R. S., & Inventado, P. (2014). Educational Data Mining and Learning Analytics. In Learning Analytics. Springer. [5] Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access. [6] Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL Institute of Education Press. [7] UNESCO (2021). Artificial Intelligence in Education: Guidance for Policy-makers. UNESCO Publishing. [8] D’Mello, S., & Graesser, A. (2013). AutoTutor and Affective Learning Systems. International Journal of Artificial Intelligence in Education. [9] Anderson, J. R., Corbett, A., Koedinger, K., & Pelletier, R. (1995). Cognitive Tutors: Lessons Learned. Journal of the Learning Sciences. [10] Holmes, W., & Tuomi, I. (2022). State of the Art in AI and Education. European Commission Joint Research Centre.

Copyright

Copyright © 2026 Shreyas Yerole, Shubham Rajhans, Tejas Shelar, Aditya Shirke, Akash Sasave. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper Id : IJRASET78794

Publish Date : 2026-03-25

ISSN : 2321-9653

Publisher Name : IJRASET

DOI Link : Click Here