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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65388

Title: PLANORA - Master Your Time, Maximize Your Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65388

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65388

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.

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Authors

PP

Pritee Patil

Computer Engineering

Zeal college of Engineering and Research.

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PK

Pratham Kadam

Computer Engineering

Zeal college of Engineering and Research.

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APASK

Asst. Prof. A. S. Khaple

Computer Engineering

Zeal college of Engineering and Research.

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SK

Sakshi Kamble

Computer Engineering

Zeal college of Engineering and Research.

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SK

Samarpita Kule

Computer Engineering

Zeal college of Engineering and Research.

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Abstract

Effective study planning is essential for students to manage academic responsibilities, yet many existing tools rely on static task lists and fixed timers that do not adapt to individual study habits. As a result, students often face difficulties in maintaining consistency, prioritizing subjects, and tracking their overall progress. To address these challenges, this paper presents Planora, an AI-powered personalized study management system developed using Python Flask, MongoDB Atlas, HTML, CSS, and JavaScript. The platform integrates task management, Pomodoro-based study sessions, notes management, session tracking, and learning analytics within a single environment. A personalized onboarding process collects user preferences and study-related information, enabling the system to provide recommendations such as best study time and priority focus subjects. Planora also incorporates the Google Gemini API to offer an AI Study Assistant along with automatic flashcard and quiz generation from uploaded study materials. Additionally, visual analytics help students monitor study patterns, productivity trends, and subject-wise learning progress. By combining personalization, productivity tools, learning analytics, and AI-assisted learning support, Planora provides a practical solution for improving study consistency and overall learning effectiveness.

KEYWORDS

Python Flask MongoDB Atlas HTML CSS and JavaScript.

Cite This Article

Pritee Patil, Pratham Kadam, Asst. Prof. A. S. Khaple, Sakshi Kamble, Samarpita Kule, "Title: PLANORA - Master Your Time, Maximize Your Learning," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026. https://doi.org/10.55041/IJSREM65388

Full Article/Conclusion

Students often face difficulties in managing their academic activities due to multiple subjects, assignments, projects, and examinations. To address these challenges, Planora was developed as an AI-powered personalized study management system that helps students organize their studies and monitor their learning progress more effectively. The platform combines study planning, productivity tracking, learning analytics, and AI-assisted learning support within a single environment. Features such as task management, Pomodoro-based study sessions, notes management, session tracking, flashcard generation, quiz generation, and the AI Study Assistant help students maintain consistency and improve their overall study experience.  

References

[1]    C. Zhou, “Design and Implementation of Personalized Learning System Based on Generative Artificial Intelligence,” in 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE), pp. 1004–1008, 2025. doi: 10.1109/EESPE63401.2025.10986914.   [2]    I. Hasan, A. A. Ahhad, A. Sabari, S. M. R. Islam, M. T. Ehsan, and M. H. Rahman, “Revolutionizing Education with Machine Learning: Personalized Learning, Tutoring Systems, and Analytics Unveiled,” in Proc. 2025 Int. Conf. on Elect., Comp. and Comm. Eng., 2025. [3]    I. Hasan, A. A. Ahhad, A. Sabari, S. M. R. Islam, M. T. Ehsan, and M. H. Rahman, “Revolutionizing Education with Machine Learning: Personalized Learning, Tutoring Systems, and Analytics Unveiled,” in Proc. 2025 Int. Conf. on Elect., Comp. and Comm. Eng., 2025. [4]    T. O. Almeida, J. F. de Magalhães Netto, and A. M. M. Lopes, “Recommendation Model of Personalized Teaching Materials in E-Learning Environments,” in Proc. 2021 IEEE Frontiers in Education Conference (FIE), pp. 1–7, 2021.   [5]    P. S. Chavan, S. N. Nikam, D. P. Chikodi, R. D. Katkar, and R. M. Mandhare, “AI-Based Study Planner,” Int. Res.J. on Advanced Engineering Hub (IRJAEH), vol. 3, no. 7, pp. 3230–3234,  Jul.   [6]    F. Ghazi, W. N. H. W. Ali, M. Mazlan, and A. Rosli, “MODORO - Pomodoro App with AI/ML for Enhanced Productivity,” Open Int. J. Inf. (OIJI), vol. 13,  no.  1,  pp.  106–118,  Jun.  2025.  doi: 10.11113/oiji2025.13n1.329.    [7]    J. Smith and R. Brown, “Generative AI in adaptive learning,” IEEE Trans. Learn. Technol., vol. 15, no.  3,  pp. 210–219,  Mar.  2023.  doi: 10.1109/TLT.2023.1234567.   [9] R. Kavade, S. Qureshi, N. Veer, V. Ugale, and P. Agrawal, "Smart Timetable System Using AI and ML," International Journal of Creative Research Thoughts (IJCRT), vol. 11, no. 5, May 2023   [8] J. Y.-K. Yau and M. S. Joy, “A Context-aware and Adaptive Learning Schedule Framework for Supporting Learners’ Daily Routines,” in Proc. Second Int. Workshop on Mobile Commun. and Learning (MCL 2007), ICONS 2007, Sainte-Luce, Martinique, 2007.

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Publication Date

6/24/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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