Home / Current Issue / Paper 1724967
SmartStudy AI: An AI-Assisted Web Platform for Academic Task Planning and Study Guidance Using Flask and Google Gemini
Subject area: Science,Engineering and Technology · Area of research: Generative AI
DOI: 10.64388/ire1724967
Abstract
Students manage lectures, assignments, examinations and competitive-exam preparation through separate notes, calendars and task lists, which makes it difficult to turn competing deadlines and limited study time into an actionable plan. SmartStudy AI is a web-based prototype that combines academic task management with conversational study guidance in a single application. The system is built with Python and Flask, uses Flask-SQLAlchemy with a MySQL database to persist users, tasks and chat records, and calls Google's Gemini model through the google-genai SDK to produce study schedules from natural-language requests. A blueprint-based architecture separates the authentication, planner, dashboard and chatbot modules, and a dedicated prompt module guides the model toward time-slotted plans that include breaks and an explicit list of tasks that do not fit. Each task stores a title, subject, priority, due date and status, and the dashboard lists the tasks of the signed-in student. During development, a generated study plan was reported to add tasks to the planner, while model-access (404) and service-unavailability (503) errors from the external API were also encountered. The application is intended to support student planning rather than replace academic judgement, and it has not yet been evaluated for learning outcomes. Calendar synchronization, automatic rescheduling and analytics are identified as future work. The prototype shows how a lightweight web stack and a hosted language model can be combined into an accessible study-planning assistant.
Keywords
study planning, task management, educational technology, large language model, Google Gemini, Flask, MySQL, student productivity
References
[1] Zimmerman BJ. Becoming a self-regulated learner: An overview. Theory Into Practice. 2002;41(2):64–70. https://doi.org/10.1207/s15430421tip4102_2
[2] VanLehn K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist. 2011;46(4):197–221. https://doi.org/10.1080/00461520.2011.611369
[3] Shao E, Guo S, Pardos ZA. Degree planning with PLAN-BERT: multi-semester recommendation using future courses of interest. Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35(17):14920–14929. https://doi.org/10.1609/aaai.v35i17.17751
[4] Atalla S, Daradkeh M, Gawanmeh A, Khalil H, Mansoor W, Miniaoui S, et al. An intelligent recommendation system for automating academic advising based on curriculum analysis and performance modeling. Mathematics. 2023;11(5). https://www.mdpi.com/2227-7390/11/5/1098
[5] Google. Gemini API: generating content. Google AI for Developers [Internet]. [cited 2026 Oct 25]. Available from: https://ai.google.dev/api/generate-content
[6] Pallets Projects. Flask documentation [Internet]. [cited 2026 Oct 25]. Available from: https://flask.palletsprojects.com/
[7] Pallets Projects. Flask-SQLAlchemy documentation [Internet]. [cited 2026 Oct 25]. Available from: https://flask-sqlalchemy.palletsprojects.com/
[8] Flask-Login contributors. Flask-Login documentation [Internet]. [cited 2026 Oct 25]. Available from: https://flask-login.readthedocs.io/
[9] Google. Google Gen AI Python SDK. GitHub [Internet]. [cited 2026 Oct 25]. Available from: https://github.com/googleapis/python-genai
[10] SQLAlchemy authors. SQLAlchemy documentation [Internet]. [cited 2026 Oct 25]. Available from: https://docs.sqlalchemy.org/
[11] Oracle. MySQL reference manual [Internet]. [cited 2026 Oct 25]. Available from: https://dev.mysql.com/doc/
[12] Google. Google Calendar API: events resource and methods [Internet]. [cited 2026 Oct 25]. Available from: https://developers.google.com/workspace/calendar/api/v3/reference/events
How to cite this paper
@article{1724967,
author = {Sure Mamatha, Aditya Chidambaram, Bhavadheesh Kapu, B Swanika, CH Gayathri},
title = {SmartStudy AI: An AI-Assisted Web Platform for Academic Task Planning and Study Guidance Using Flask and Google Gemini},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {2094-2101},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724967.pdf},
abstract = {Students manage lectures, assignments, examinations and competitive-exam preparation through separate notes, calendars and task lists, which makes it difficult to turn competing deadlines and limited study time into an actionable plan. SmartStudy AI is a web-based prototype that combines academic task management with conversational study guidance in a single application. The system is built with Python and Flask, uses Flask-SQLAlchemy with a MySQL database to persist users, tasks and chat records, and calls Google's Gemini model through the google-genai SDK to produce study schedules from natural-language requests. A blueprint-based architecture separates the authentication, planner, dashboard and chatbot modules, and a dedicated prompt module guides the model toward time-slotted plans that include breaks and an explicit list of tasks that do not fit. Each task stores a title, subject, priority, due date and status, and the dashboard lists the tasks of the signed-in student. During development, a generated study plan was reported to add tasks to the planner, while model-access (404) and service-unavailability (503) errors from the external API were also encountered. The application is intended to support student planning rather than replace academic judgement, and it has not yet been evaluated for learning outcomes. Calendar synchronization, automatic rescheduling and analytics are identified as future work. The prototype shows how a lightweight web stack and a hosted language model can be combined into an accessible study-planning assistant.},
keywords = {study planning, task management, educational technology, large language model, Google Gemini, Flask, MySQL, student productivity},
month = {November},
doi = {https://doi.org/10.64388/ire1724967}
}