LingoFix AI

Inspiration

Language learners often receive a corrected sentence without fully understanding why their original sentence was wrong. This can lead to repeating the same mistakes.

With my background in Linguistics Engineering, AI, and Natural Language Processing, I wanted to create a tool that goes beyond simple correction. This inspired me to build LingoFix AI, an AI-powered linguistic learning assistant that helps learners understand their mistakes and practice them.

What It Does

LingoFix AI analyzes a learner's sentence and:

• Detects the main linguistic error
• Classifies the error into a linguistic category
• Provides the corrected sentence
• Explains the mistake in simple language
• Generates a personalized multiple-choice practice question

The goal is to help learners move from "What is wrong?" to "Why is it wrong, and how can I improve?"

How We Built It

LingoFix AI was built using:

• Python for the application logic
• Streamlit for the interactive web interface
• OpenAI API for AI-powered linguistic analysis and content generation
• python-dotenv for environment configuration

The workflow is:

Learner Input → AI Analysis → Error Detection → Error Classification → Correction → Explanation → Personalized Practice

A structured prompt guides the AI to return consistent sections for the error, category, correction, explanation, and practice question. Streamlit then presents this information in a simple and user-friendly interface.

Challenges

One of the main challenges was designing the AI output so that it could provide useful linguistic feedback while remaining simple and understandable for learners.

Another challenge was building a working MVP within a limited time while connecting the AI model, application interface, and personalized practice into one complete workflow.

What I Learned

Through this project, I learned how to combine linguistics with AI and software development to create an educational application.

I also learned more about designing structured AI prompts, connecting an AI API to a Python application, and turning AI-generated responses into a practical user experience.

Accomplishment

The biggest accomplishment was building a working end-to-end MVP that transforms a single learner sentence into structured linguistic analysis, correction, explanation, and personalized practice.

LingoFix AI demonstrates how AI can be combined with linguistic knowledge to make language learning more interactive and personalized.

What's Next

Future versions could include:

• Support for multiple languages
• Learner progress tracking
• More advanced linguistic error categories
• Adaptive difficulty levels
• Personalized learning history
• More interactive exercises

The long-term goal is to turn LingoFix AI from a correction tool into a personalized linguistic learning companion.

Built With

• api
• artificial
• intelligence
• language
• linguistics
• natural
• natural-language-processing
• openai
• processing
• python
• streamlit