TaskBuddy: A Smart Task Scheduling and Reminder Mobile Application

T. SwapnaAssistant Professor, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology & Science, Hyderabad, IndiaG. Adithi RaoDepartment of Computer Science and Engineering, G. Narayanamma Institute of Technology & Science, Hyderabad, IndiaA. AnanyaDepartment of Computer Science and Engineering, G. Narayanamma Institute of Technology & Science, Hyderabad, IndiaS. YasaswiniDepartment of Computer Science and Engineering, G. Narayanamma Institute of Technology & Science, Hyderabad, India

Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 86-95

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 07-05-2026

doi Logo doi.org/10.47001/IRJIET/2026.105012

Abstract

The Smart Task Scheduler and Reminder App is a proposed cross-platform mobile application designed to help users efficiently manage their time and daily activities. It enables users to create personalized schedules or choose automatically generated ones that suggest optimal timings based on deadlines, task importance, and available free time. By providing timely reminders and a flexible, user-friendly interface, the app aims to enhance productivity, reduce stress, and promote a balanced routine. It focuses on adaptability, allowing users to modify schedules according to their preferences while maintaining overall efficiency and organization.

Most existing task management systems depend heavily on manual scheduling and assume users already have fixed calendars. They often lack intelligent adaptability, dynamic rescheduling, and awareness of users’ real free time or changing priorities. To address these limitations, the proposed system integrates automation and intelligent task allocation. The app will be developed using Flutter for cross-platform compatibility, Firebase or SQLite for data management, and a Python (Flask/FastAPI) or Dart-based backend for implementing scheduling algorithms. These technologies together will enable real-time synchronization, automated reminders, and smart task prioritization, providing a practical and efficient solution for modern time management.

Keywords

Task Scheduling, Time Management, Productivity, Flutter, Intelligent Scheduling, Mobile Application


Citation of this Article

T. Swapna, G. Adithi Rao, A. Ananya, & S. Yasaswini. (2026). TaskBuddy: A Smart Task Scheduling and Reminder Mobile Application. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 86-95. Article DOI https://doi.org/10.47001/IRJIET/2026.105012

References
  1. J. Lewis and B. Dunn, Android Programming: The Big Nerd Ranch Guide, 5th Edition, Big Nerd Ranch, Atlanta, 2019.
  2. B. Aeon, H. Aguinis, R. Cropanzano, et al., “Boosting Productivity and Wellbeing Through Time Management,” Frontiers in Education, Vol. 10, pp. 1–15, 2025.
  3. B. Haderer and M. Ciolacu, “Education 4.0: Artificial Intelligence Assisted Task-and Time Planning System,” International Conference on Interactive Collaborative Learning, pp. 1–8, 2022.
  4. G. Leshed and P. Sengers, “The Digital Architecture of Time Management,” Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 1–12, April 2018.
  5. H. Khiat, “Using Automated Time Management Enablers to Improve Self-Regulated Learning,” Active Learning in Higher Education, Vol. 20, pp. 1–12, 2019.
  6. J. Liu, H. Zhang, M. Chen, et al., “ML-Driven Scheduling Advances,” Artificial Intelligence Review, Vol. 58, pp. 1–18, 2025.
  7. N. Pillay and R. Qu, “A Survey of Computational Intelligence in Educational Timetabling,” IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 5, pp. 1–17, March 2021.
  8. R. Santos, A. Martinez, K. Johnson, et al., “The Relevance of Time Management in Academic Achievement: A Critical Review of the Literature,” Journal of Educational Research and Practice, Vol. 14, pp. 1–15, 2024.
  9. S. Russell and P. Norvig, “Automated Planning and Scheduling (APS),” Artificial Intelligence: A Modern Approach – Research Survey, Vol. 1, pp. 1–20, 2025.
  10. X. Chen, Y. Wang, L. Zhang, et al., “Dependency-Aware Joint Task Offloading and Resource Allocation,” IEEE Transactions on Wireless Communications, Vol. 23, pp. 1–14, 2024.
  11. Y. Wang, X. Li, T. Zhou, et al., “Intelligent Scheduling Optimization Framework,” Journal of Intelligent Systems, Vol. 34, pp. 1–16, 2025.