AI Study Planner

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

Inspiration

Planning a long-term learning journey can become difficult when there are many things to manage at the same time.

I was trying to balance programming, DSA, AI/ML, projects, university exams, and other technical skills. I could create a large roadmap, but turning that roadmap into realistic monthly, weekly, and daily tasks was much harder.

That led to the idea for AI Study Planner.

Instead of manually creating hundreds of study tasks, the user can describe what they want to learn, how much time they have available, their priorities, goals, projects, and target duration.

The application uses a locally running AI model to turn those requirements into a structured study plan.

A key part of the idea was keeping the user in control. The AI does not immediately modify the tracker. The generated plan is first presented for review, and only after approval is it implemented into the application.

The goal is simple:

Turn learning goals into an actionable plan without taking control away from the learner.

What it does

AI Study Planner is an AI-assisted study and career planning application that converts learning goals into an organized:

Month → Week → Day → Tasks

The user can provide information such as:

• Skills they want to learn
• Existing knowledge
• Available study time
• Learning priorities
• Target completion period
• Projects they want to build
• Career-related goals
• Other commitments

The local AI analyzes these requirements and generates a structured study plan.

The workflow has three main stages:

1. Describe

The user describes their learning goals and constraints.

2. Generate & Review

The AI generates monthly goals, weekly objectives, and daily tasks.

The user can inspect the generated plan before making any changes to their tracker.

3. Implement

After approval, the plan is converted into actual tracker tasks.

The application also provides:

• AI-assisted study plan generation
• Monthly, weekly, and daily planning
• Task tracking and completion
• Progress tracking
• Streak tracking
• Focus Mode
• Daily study organization
• Review-before-implementation workflow
• Local AI inference using Ollama and Gemma
• Open-source project structure

The important distinction is that AI planning and task tracking are separate steps. The AI suggests a plan, while the user decides what actually gets added to the tracker.

How I built it

The project was built as a web-based application using standard web technologies combined with local AI inference.

Technologies Used

• HTML
• CSS
• JavaScript
• Node.js
• Ollama
• Gemma
• Git
• GitHub
• Antigravity for AI-assisted development

Local AI Architecture

The study-planning functionality uses Gemma through Ollama.

Instead of relying on a paid external AI API, the application communicates with an Ollama instance running locally on the user's computer.

The basic architecture is:

User Input

↓

AI Study Planner

↓

Local Ollama API

↓

Gemma

↓

Structured Study Plan

↓

User Review

↓

Approve / Modify

↓

Implement into Tracker

↓

Monthly → Weekly → Daily Tasks

This approach allows the AI component to run locally while keeping the application's planning workflow under the user's control.

Development with Antigravity

I used Antigravity as an AI-assisted development environment throughout the project.

It helped with:

• Application development
• Feature implementation
• Debugging
• Testing
• Performance improvements
• Project organization
• Local AI integration
• Documentation
• Code cleanup

Antigravity was used as a development assistant. It is separate from the AI that powers the final study-planning functionality.

The study-planning AI itself runs through Ollama + Gemma.

Project Structure

The project is organized so that application code, services, data, assets, and documentation remain separated.

A simplified structure is:

The project does not contain my personal study data or personal tracker information.

Running the AI Locally

First, install Ollama and verify the installation:

Then download the required Gemma model:

Verify that the model is available:

You can also test Gemma directly:

Once Ollama and Gemma are running locally, the application can communicate with the local AI service.

Running the Project

Clone the repository:

Open the project in your preferred development environment and start it using the project's normal local development method.

For the AI planning functionality, Ollama must also be installed and the required Gemma model must be available locally.

Demo

Video Demo:
https://youtu.be/smpteorWVU0

** Live Local Demo :** Open
[https://akshay118r.github.io/AI-Study-Planner/ ] in any modern browser.

The demo shows the application interface, study-planning workflow, tracker functionality, and integration of the AI-assisted planning process.

The AI functionality requires Ollama and the required Gemma model to be installed locally.

Code

GitHub Repository:
https://github.com/akshay118R/AI-Study-Planner

The source code is available for developers to inspect, run locally, modify, and contribute to.

Challenges we ran into

One of the main challenges was designing the application so that AI-generated plans were actually useful instead of simply producing a long list of generic tasks.

A study plan needs to consider multiple factors at the same time, including:

• Available study time
• Learning priorities
• Target duration
• Existing knowledge
• Multiple subjects or skills
• Projects and other goals

Another challenge was deciding how the AI should interact with the tracker.

Automatically inserting AI-generated tasks could make the application unpredictable. Instead, the project was designed around a review-before-implementation workflow.

The AI generates the plan first.

The user reviews it.

The user decides whether to approve or modify it.

Only then is it implemented into the tracker.

Integrating a locally running model also introduced additional considerations around Ollama availability, model installation, communication with the local service, and differences in inference performance depending on the user's hardware.

Finally, building and debugging the application while using an AI-assisted development environment required continuous testing and review. Generated implementations still needed to be inspected, tested, and adjusted to match the intended behavior.

Accomplishments we're proud of

One of the biggest accomplishments was turning a simple study-planning idea into a functional application that combines AI planning with actual task management.

The project goes beyond a traditional to-do list by connecting:

AI Planning + Study Management + Progress Tracking

We are particularly proud of the review-before-implementation workflow.

The AI does not directly take over the user's schedule. Instead, it acts as a planning assistant and allows the user to make the final decision.

Another accomplishment is integrating Gemma through Ollama so that the planning functionality can run locally without requiring a proprietary cloud AI API.

This demonstrates how an open-weight model can be integrated into a practical application rather than being used only as a standalone chatbot.

The project is also structured as an open-source GitHub repository, allowing other developers to inspect the implementation, experiment with the AI planning logic, and contribute improvements.

What we learned

This project taught us that integrating AI into an application is not just about connecting a model to an input box.

The surrounding workflow is equally important.

We learned how to:

• Integrate a locally running AI model into a web application
• Work with Ollama and Gemma
• Design prompts for structured AI output
• Separate AI generation from application state changes
• Build a review-and-approval workflow
• Organize a web application into maintainable components
• Debug and test AI-assisted implementations
• Use an AI coding agent as part of the development process
• Think about privacy when designing AI-powered applications
• Build an open-source project that can be run locally by other developers

We also learned that AI-generated results need to be treated as suggestions that should be reviewed, especially when they are being converted into real tasks or schedules.

The combination of a local AI model and an application workflow can provide a useful balance between automation and user control.

What's next

There are several areas we would like to improve in future versions.

Planned improvements include:

• Support for additional local AI models
• Better study-plan customization
• More advanced learning-time estimation
• Automatic workload balancing
• More detailed progress analytics
• Improved project planning
• Additional tracker views
• Better model configuration
• More personalization options
• More intelligent scheduling based on completed tasks and progress

The longer-term goal is to make the planner more adaptive.

Instead of generating a plan once, future versions could continuously analyze progress and help adjust upcoming tasks while still keeping the learner in control of major changes.

The project will remain focused on the combination of open-source software, local AI, practical study management, and user-controlled planning.

Prize Categories

• Open-source AI
• Local AI / Local Inference