Build an AI Study Assistant with Claude Code + Android Studio

Build an AI Study Assistant with Claude Code + Android Studio

Think about constructing a full Android app that generates AI questions, runs on an actual backend, and makes use of a database with out writing a single line of code. Claude Code, Anthropic’s terminal-based assistant, makes it potential to ship a working product from one clear immediate.

This tutorial reveals easy methods to create an AI Examine Assistant that ingests PDFs and produces exam-ready questions, MCQs, and summaries for JEE, NEET, GATE, and boards. On this article, you will note the complete workflow in motion.

What we’ll be constructing:

  • Backend – Node.js + Typescript + Specific with RESTful API’s 
  • Database – SQLite for doc/session administration 
  • AI Engine – Claude’s API for clever content material creation 
  • Cell Software – Android & MVVM architected 
  • Extra Options – 12 examination ranges, classification/scores of query issue ranges, Versioned AI prompts 
Claude Code

Palms on

With out additional ado, let’s soar into the method:

Step 1: Challenge Setup

Open your terminal and initialize the mission: 

cd ~/Paperwork/Tasks 
mkdir StudyAssistant 
cd StudyAssistant 
claude 

Step 2: The Magic Immediate

Write an in depth single immediate to construct the AI Assistant within the command line interface of Claude code: 

AI Examine Assistant: Construct an AI agent that converts uploaded notes or PDFs into exam-specific questions, MCQs/MSQs, and revision summaries after the person selects an examination stage. 

Movement: Add  choose examination  analyze notes  generate exam-aligned content material. 

Necessities: Questions, MCQs/MSQs (with right solutions), and summaries should match examination issue and keep grounded within the notes solely. 

Backend Tech

Node.js, TypeScript, Specific, SQLite

Frontend

Android app with minimal UI

AI Layer

Claude for content material technology

Core Options

Versioned prompts, PDF parsing

API Endpoints

/doc/add
/examination/choose
/content material/generate
/historical past

Aim: Ship a clear, end-to-end, exam-focused examine assistant. 

Step 3: Auto-Generated Backend

Claude-Code begins working and generates this production-ready construction: 

Directory structure

The important thing options of this auto-generated backend are: 

  • Examination Configurations: Configuration choices embody 12 forms of examinations, together with (JEE/NEET/GATE/UPSC/BOARD) with individually outlined issue ranges. 
  • Versioned Prompts: Prompts are saved in JSON format; due to this fact, when altering the system, you may replace your prompts with out compromising the performance of the present model of the applying. 
  • Clear Structure: All of our providers are designed to comply with the one accountability precept and shall be straightforward to check and prolong. 

Step 4: RESTful API Endpoints

Claude code helped in creating the API endpoints as effectively in line with our necessities. These endpoints will assist in the sleek working of our app. 

API Endpoints

The workflow of the app in line with the endpoints shall be as follows: 

  1. Person uploads their doc could possibly be PDF or in docx format. 
  2. Then, accordingly they’ll choose the extent of the examination JEE, NEET or any stage of faculty semester. 
  3. Click on ‘Generate Query’, the endpoint calls the claude API to generate questions and summarize the notes as effectively. 
  4. Person can now view the generated query (MCQs, numericals, coding questions) with the completely different stage of issue (low, medium, excessive). 
App workflow for Claude Code

Step 5: Establishing the Android App (MVVM Structure)

Claude Code generated a separate Android folder, having all of the information required to setup the android app. Following is the construction it created for the Android App: 

Directory structure for study assistant

Right here, the ApiClient.kt, is the principle file because it offers the repository sample which is used as a single supply of reality for knowledge.  

non-public const val BASE_URL = " // Emulator localhost 

Step 6: Operating the Software

All the codebase construction of backend, databased and android app has been created, now we’ll run the backend first. You should use the next instructions to initialize the backend server: 

cd backend 
npm set up 
npm run dev 

Output:  

AI Examine Assistant API operating on port 3000
Surroundings: growth
Database: ./database/study_assistant.db

As soon as the backend server has been setup, we’ll launch the android app now.  

  1. Open Android Studio. Go to ‘file’ within the topbar, click on on open and choose your ‘Android’ folder created by Claude Code. 
Android Studio Dashboard
  1. Click on on Gradle sync as it is going to sync all of the code to run our app on an emulator (digital machine). 
  2. Go to the Machine supervisor in ‘Instruments’ and choose any digital machine or if you wish to run the app by yourself machine then both you may join your machine through wifi or utilizing a USB. 
Selecting Virtual Device in Android Studio
  1. As soon as the machine has been related, click on on ‘Run’. 

Step 7: Run the applying (Demo)

You’ll see a digital machine operating on the proper sidebar, click on on that and choose your software ‘AI Examine Assistant’

How are you going to Customise it Additional?

  1. Add new examination ranges like CAT, SAT, JEE Superior and so forth by merely enhancing the backend file your self or asking Claude Code to do it for you. 
sat: { 
  id: 'sat', 
  title: 'SAT', 
  questionTypes: ['mcq', 'numerical'], 
  difficultyDistribution: { straightforward: 30, medium: 50, laborious: 20 }, 
  questionCount: { mcq: 30, numerical: 15 } 
}
  1. Ask the agent to make extra artistic questions by experimenting with the immediate, temperature and completely different parameters. 
{
  "systemPrompt": "You might be an knowledgeable {{examLevel}} query setter...", 
  "temperature": 0.7, 
  "maxTokens": 2000 
}
  1. You’ll be able to change the UI theme, making it extra skilled and superior by enhancing android/app/src/essential/res/values/colours.xml
#1976D2   

Key Takeaways: Mastering AI-Assisted Improvement

Listed here are the important thing elements that we have to perceive after now we have constructed the mission or for our future tasks: 

Side Particulars
Immediate Engineering is Important Dangerous Immediate: “Construct a examine app”
Good Immediate: “Construct production-ready AI Examine Assistant with Node.js, SQLite, Android MVVM, 12 examination ranges, versioned prompts, clear structure”
What Claude Code Excels At Boilerplate code technology
Following established patterns (MVVM, REST APIs)
Constant naming conventions
Modular structure
API endpoint creation
Database schema design
What Nonetheless Wants Human Overview Safety implementations (API keys, enter validation)
Edge case dealing with
Efficiency optimization at scale
Advanced enterprise logic
Manufacturing monitoring and logging

Conclusion

The event course of has grow to be extra environment friendly which leads to full system transformation. The barrier to entry for constructing refined apps has dropped as a result of current superior applied sciences allow customers to create complicated functions with no need programming abilities. College students and entrepreneurs now have the power to create the instruments which they beforehand might solely think about. 

The no-code revolution is right here. After the AI Examine Assistant, what is going to you construct subsequent?

Ceaselessly Requested Questions

Q1. What does this AI Examine Assistant app do?

A. It converts uploaded PDFs into exam-specific questions, MCQs/MSQs with solutions, and revision summaries based mostly solely on the notes.

Q2. What tech stack does the mission use?

A. Node.js + TypeScript + Specific for backend, SQLite for storage, Claude API for technology, and an Android MVVM app for the shopper.

Q3. How can I customise the assistant for brand new exams?

A. Add a brand new examination config within the backend and replace the versioned immediate settings to manage query varieties, issue combine, and output fashion.

Riya Bansal

Knowledge Science Trainee at Analytics Vidhya
I’m at the moment working as a Knowledge Science Trainee at Analytics Vidhya, the place I deal with constructing data-driven options and making use of AI/ML strategies to resolve real-world enterprise issues. My work permits me to discover superior analytics, machine studying, and AI functions that empower organizations to make smarter, evidence-based choices.
With a powerful basis in pc science, software program growth, and knowledge analytics, I’m enthusiastic about leveraging AI to create impactful, scalable options that bridge the hole between expertise and enterprise.
📩 You may as well attain out to me at [email protected]

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