README.md

metadata

title: AI Reading Engagement
emoji: 🏆
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.42.0
app_file: app.py
pinned: false

📖 AI-Powered Adaptive Reading Engagement Model

🔍 Understanding Social Cognition & Reading Behavior in a Digital World

📝 Overview

The shift towards digital reading presents challenges and opportunities in how people engage with text. Many students struggle with reading motivation, comprehension, and goal-setting in digital environments. This project aims to analyze and enhance reading engagement using AI-powered Natural Language Processing (NLP), sentiment analysis, and emotion detection to create an adaptive reading system.

🎯 Objectives

✔ Analyze reading motivation & engagement in digital environments. ✔ Detect emotions & sentiment from reading interactions. ✔ Develop an adaptive reading model that adjusts text difficulty based on comprehension. ✔ Encourage goal-setting in reading behavior using AI-generated feedback.

This project aligns with research on social cognition and motivation sciences, contributing to educational psychology, digital literacy, and human-AI interaction studies.

🚀 Features

🔹 Sentiment & Emotion Detection – Uses NLP models to analyze user engagement. 🔹 Adaptive Reading Level Detection – Adjusts difficulty based on comprehension. 🔹 Reading Goal Tracker – Helps users track reading progress. 🔹 AI-Powered Discussion Prompts – Generates thought-provoking questions to enhance engagement. 🔹 Visual Analytics Dashboard – Displays engagement trends and sentiment scores.

🧠 How the AI Works

1️⃣ User selects or enters a text passage. 2️⃣ AI analyzes the text using sentiment analysis (positive/negative/neutral). 3️⃣ AI detects emotional engagement (e.g., joy, frustration, curiosity). 4️⃣ System suggests adaptive modifications (simpler text for struggling readers, more complex text for advanced users). 5️⃣ AI generates reflection prompts to encourage deeper engagement. 6️⃣ A dashboard visualizes trends in reading engagement over time.

💡 Research Applications

This project can be used in: ✔ Educational research – Understanding students’ digital reading behavior. ✔ Cognitive psychology – Studying motivation & attention in reading. ✔ AI in education – Exploring AI-enhanced literacy interventions. ✔ Human-AI interaction – Investigating how AI influences reading engagement.

📌 Installation Guide

To run the app locally:

1️⃣ Clone this repository

git clone https://huggingface.co/spaces/Durganihantri/AI-Reading-Engagement cd AI-Reading-Engagement

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the app

streamlit run app.py

4️⃣ View the app in your browser Go to http://localhost:8501/

📊 Example Output

🔹 Input Text: “Reading is an essential skill that shapes our understanding of the world.”

🔹 Sentiment Analysis: ✅ Positive: 85% | ❌ Negative: 5% | ⚪ Neutral: 10%

🔹 Emotion Detection: 🎭 Curiosity (70%), Engagement (65%), Frustration (5%)

🔹 Adaptive Reading Suggestion: 📖 “Try reading a related article on critical thinking in reading.”

🔹 AI-Generated Reflection Prompt: 🤔 “How does reading shape your perception of reality?”

🌍 Try It Online

👉 Live Demo on Hugging Face

🛠 Technologies Used • Streamlit – Frontend UI • Transformers (Hugging Face) – NLP models for sentiment & emotion detection • NLTK & spaCy – Text preprocessing & sentiment analysis • Matplotlib – Data visualization • Python – Backend scripting

🤝 Contributing

We welcome contributions! You can: ✔ Improve the adaptive learning model. ✔ Enhance data visualization & analytics. ✔ Expand the AI’s ability to suggest more personalized reading insights.

Fork the repository and submit a pull request!

📜 License

This project is licensed under the MIT License.

📬 Contact

🔹 Author: Durganihantri 🔹 Email: [email protected] 🔹 LinkedIn: http://linkedin.com/in/durganihantri

🚀 Future Enhancements

✅ Add real-time emotion tracking for interactive reading. ✅ Integrate text-to-speech AI for multi-modal engagement. ✅ Expand to multilingual support for global accessibility.