AI Powered Habit/Behaviour Visualizer among Students
Authors/Creators
Description
Students often struggle to maintain consistent habits due to limited feedback, lack of timely guidance, and difficulty visualizing their own progress. To address these challenges, this paper presents the rationale for an AI-driven, web-based student habit tracker designed to provide continuous monitoring, intelligent insights, and personalized recommendations. By integrating modern web technologies with AI-powered analytics, the system transforms raw habit data into meaningful patterns—such as completion trends, streaks, and time-based performance summaries—that help students make informed adjustments to their routines. The platform delivers real-time nudges, contextual suggestions, and adaptive goal-setting support, ultimately fostering self-regulation, accountability, and long-term behavioral improvement. This rationale establishes the need for such a tool in academic environments and highlights its potential to enhance student productivity, motivation, and sustained habit formation.
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AI Powered HabitBehaviour Visualizer -HBRP Publication.pdf
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References
- 1. J. Clear, Atomic Habits: An Easy & Proven Way to Build Good Habits and Break Bad Ones. Penguin, 2018. (Foundational theory on habit formation)
- 2. B. J. Fogg, "A Behavior Model for Persuasive Design," in Proceedings of the 4th International Conference on Persuasive Technology, ACM, 2009, pp. 1–7. (Behavior model commonly used in habit-tracking systems)
- 3. D. C. Clary and A. R. Karlin, "Digital Habit Tracking and Its Effect on Student Productivity," Journal of Educational Technology Development, vol. 14, no. 2, pp. 45–58, 2021. (Study on digital habit tracking for students)
- 4. OpenAI, "GPT-Based Recommendation and Insight Generation," OpenAI Technical Documentation, 2024. [Online]. Available: https://openai.com (Technical documentation for AI-powered suggestions)
- 5. T. Rodgers and L. Miller, "Learning Analytics for Behaviour Monitoring in Education," IEEE Transactions on Learning Technologies, vol. 15, no. 4, pp. 520–532, Oct. 2022. (Used for insights and pattern identification)
- 6. MongoDB Inc., MongoDB: The Definitive Guide, 3rd ed. O'Reilly Media, 2022. (Database source for storing habit logs)
- 7. Next.js Team, "Next.js Documentation," 2024. [Online]. Available: https://nextjs.org (Framework used for web-based habit tracker)
- 8. M. Al-Khalifa and S. Al-Mansour, "AI-Enabled Educational Systems: A Review of Personalized Learning Approaches," Education and Information Technologies, vol. 28, pp. 345–368, 2023. (Background on AI personalization in education)
- 9. J. Duckett, HTML & CSS: Design and Build Websites. Wiley, 2019. (Frontend technologies reference)
- 10. A. R. Alqahtani, "Performance Evaluation of Web-Based Distributed Systems," IEEE Transactions on Services Computing, vol. 15, no. 2, pp. 350–362, Mar. 2022. (Performance reference relevant to system analysis)