Improve dataset card: Add comprehensive metadata, paper/code links, detailed overview, key findings, methodology, results, installation, and sample usage

README.md

@@ -1,5 +1,15 @@
 ---
-license: mit
+license: cc-by-4.0
+task_categories:
+- text-classification
+- text-generation
+language:
+- en
+tags:
+- sentiment-analysis
+- education
+- generative-ai
+- app-reviews
 dataset_info:
   features:
   - name: app_name
@@ -206,30 +216,475 @@ configs:
 ---
 
 ---
+<div align="center">
 
-# GenAI-EdSent: Sentiment-Driven Evaluation of AI Educational Apps
+<img src="https://github.com/erfan-nourbakhsh/GenAI-EdSent/raw/main/Figures/logo.png" alt="GenAI-EdSent Logo" width="350"/>
 
-The integration of Generative AI into education has sparked a digital transformation in e-teaching, yet user perceptions of AI educational apps remain critically underexplored. This research project bridges that gap through a comprehensive sentiment-driven evaluation of user reviews from top AI educational apps on the Google Play Store.
-https://github.com/erfan-nourbakhsh/GenAI-EdSent
+# 🎓 GenAI-EdSent: Sentiment-Driven Evaluation of AI Educational Apps
 
-The paper is under review right now.
+[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://www.python.org/downloads/)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/erfan-nourbakhsh/GenAI-EdSent/blob/main/LICENSE)
+[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://huggingface.co/papers/2512.11934)
+[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Dataset-yellow.svg)](https://huggingface.co/datasets/Erfan-Nourbakhsh/GenAI-EdSent)
+[![Status](https://img.shields.io/badge/Status-Active-success.svg)](https://github.com/erfan-nourbakhsh/GenAI-EdSent)
 
-## Dataset
+*Unveiling User Perceptions in the Generative AI Era Through Large-Scale Sentiment Analysis*
+
+</div>
+
+---
+
+## 📖 Overview
+
+The integration of **Generative AI** into education has sparked a digital transformation in e-teaching, yet user perceptions of AI educational apps remain critically underexplored. This research project bridges that gap through a comprehensive **sentiment-driven evaluation** of user reviews from top AI educational apps on the Google Play Store.
+
+This dataset accompanies the paper [Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of E-Teaching](https://huggingface.co/papers/2512.11934).
+
+Code: [https://github.com/erfan-nourbakhsh/GenAI-EdSent](https://github.com/erfan-nourbakhsh/GenAI-EdSent)
+
+### 🎯 Research Objectives
+
+1.  **Quantify sentiment distributions** and distill key positive/negative themes across app categories
+2.  **Compare performance trends** among different app types (homework helpers, math solvers, LMS, etc.)
+3.  **Propose future directions** for AI educational ecosystems with hybrid AI-human models
+
+### 📄 Associated Paper
+
+**Title:** *Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of E-Teaching*
+
+**Authors:** Adeleh Mazaheriyan (Islamic Azad University) & Erfan Nourbakhsh (University of Isfahan)
+
+**Abstract:** This study performs a sentiment-driven evaluation of user reviews from 22 top AI ed-apps on the Google Play Store to assess efficacy, challenges, and pedagogical implications. Our pipeline leverages **RoBERTa** for binary sentiment classification, **GPT-4o** for key point extraction, and **GPT-5** for synthesizing top positive/negative themes. Results reveal predominantly positive sentiments, with homework apps leading (e.g., Edu AI: 95.9% positive) while specialized LMS/language apps lag due to stability issues.
+
+---
+
+## 📊 Research at a Glance
+
+<div align="center">
+
+| 📱 Apps Analyzed | 💬 Reviews Processed | 🤖 AI Models Used | 📚 Categories |
+|:----------------:|:-------------------:|:----------------:|:-------------:|
+| **22** | **481,000+** | **3** (RoBERTa, GPT-4o, GPT-5) | **7** |
+
+</div>
+
+<div align="center">
+
+### 🔥 Quick Stats
+
+🎯 **96.0%** positive sentiment for top performer (Edu AI)
+⚡ **481K** training reviews • **481K** validation samples
+📈 **4.43M** total ratings across all apps
+🏆 **85%** average positive sentiment for homework helpers
+⚠️ **21.8%** positive sentiment for lowest performer (Teacher AI)
+
+</div>
+
+---
+
+## 🔍 Key Findings
+
+### 📊 Sentiment Distribution Highlights
+
+-   **Homework Helpers** dominate with ~85% positive sentiment
+    -   🥇 **Edu AI**: 96.0% positive (accuracy, speed, personalization)
+    -   🥈 **Answer.AI**: 92.7% positive (24/7 tutoring, step-by-step explanations)
+
+-   **Math-Focused Solvers** show strong performance (~80% positive)
+    -   Users praise problem-solving efficiency and photo recognition
+
+-   **Language/LMS Apps** lag behind (20-40% positive)
+    -   ⚠️ **Teacher AI**: 21.8% positive (instability, limited features)
+    -   Issues: crashes, paywalls, feature gaps
+
+### 🌟 Top Positive Themes
+
+1.  ⚡ **Efficiency & Speed** - Quick solutions for homework and brainstorming
+2.  🎯 **Personalized Learning** - Step-by-step explanations tailored to student needs
+3.  🎮 **Engagement** - Gamification and community features boost motivation
+4.  🌍 **Accessibility** - Democratizing education for under-resourced areas
+5.  🤝 **Multi-Subject Support** - Versatile tools across STEM and humanities
+
+### ⚠️ Top Negative Themes
+
+1.  💰 **Aggressive Monetization** - Restrictive paywalls limiting free features
+2.  ❌ **Inaccuracies** - Wrong answers eroding trust, especially in specialized domains
+3.  📺 **Excessive Ads** - Disrupting learning flow
+4.  🐛 **Technical Glitches** - Crashes, slow loading, notation failures
+5.  ⚖️ **Equity Concerns** - Digital divide and over-reliance risks
+
+---
+
+## 🛠️ Methodology
+
+Our systematic pipeline combines **web scraping**, **transformer-based NLP**, and **large language models** to analyze authentic user feedback at scale.
+
+<div align="center">
+
+![Methodological Approach](https://github.com/erfan-nourbakhsh/GenAI-EdSent/raw/main/Figures/approach.png)
+*Figure 1: Multi-stage workflow from data collection to theme synthesis*
+
+</div>
+
+### 🔄 Pipeline Stages
+
+#### 1️⃣ **App Selection & Categorization**
+-   Selected **22 prominent AI education apps** based on ratings, downloads, and GenAI integration
+-   Categorized into **7 functional types** with overlaps for multifunctional designs:
+    -   📝 AI Quiz & Question Generators
+    -   🎒 All-in-One Study Companions
+    -   ✏️ Homework Helpers
+    -   🔢 Math-Focused Solvers & Specialized Tools
+    -   📄 Document/Content Tools
+    -   🏫 Learning Management Systems (LMS)
+    -   🌐 Language Learning Apps
+
+#### 2️⃣ **Data Collection**
+-   **Web scraping** from Google Play Store using Python
+-   Collected app metadata + verbatim user reviews (up to November 2025)
+-   Dataset: **Tens of thousands to millions** of reviews per app
+
+#### 3️⃣ **Sentiment Analysis & Theme Extraction**
+
+| Stage | Model | Purpose |
+|-------|-------|---------|
+| **Binary Classification** | [RoBERTa](https://arxiv.org/abs/1907.11692) | Transformer-based sentiment labeling (positive/negative) |
+| **Key Point Extraction** | [GPT-4o](https://arxiv.org/abs/2410.21276) | Distill recurring themes and pain points |
+| **Theme Synthesis** | GPT-5 | Generate top 5 positive/negative summaries |
+
+#### 4️⃣ **Aggregation & Trend Analysis**
+-   Computed sentiment percentages per app and category
+-   Enabled cross-app performance comparisons
+
+---
+
+## 📈 Results
+
+<div align="center">
+
+![Sentiment Analysis Results](https://github.com/erfan-nourbakhsh/GenAI-EdSent/raw/main/Figures/results.png)
+*Figure 2: Positive vs. negative sentiment percentages across 22 AI educational apps*
+
+</div>
+
+### 🏆 Top Performers
+
+| Rank | App | Category | Positive % |
+|------|-----|----------|-----------|
+| 1 | Edu AI | Homework Helper | **96.0%** |
+| 2 | Answer.AI | Multi-Tool | **92.7%** |
+| 3 | Question.AI | Chatbot/Math | **87.9%** |
+| 4 | Homework AI | Math/Essay | **85.6%** |
+| 5 | Help AI | Homework Helper | **85.2%** |
+
+### ⚠️ Areas for Improvement
+
+| Rank | App | Category | Positive % | Key Issues |
+|------|-----|----------|-----------|------------|
+| 22 | Teacher AI | Language | **21.8%** | Instability, limited features |
+| 21 | Tutor AI | Learning Assistant | **41.7%** | Narrow functionality |
+| 20 | Blackboard | LMS | **53.3%** | Technical glitches |
+
+### 📊 Category-Level Insights
+
+-   **Homework Helpers**: Average 85% positive → Strong personalization and speed
+-   **Math Solvers**: Average 80% positive → Photo recognition praised, notation failures criticized
+-   **LMS/Language Apps**: Average 35% positive → Urgent need for stability improvements
+
+---
+
+## 💡 Future Directions
+
+### 🔮 Proposed Innovations
+
+1.  **Hybrid AI-Human Models** 🤝
+    -   Combine app strengths (real-time assistance) with teacher oversight
+    -   Mitigate risks like inaccuracies and over-dependency
+
+2.  **VR/AR Integration** 🥽
+    -   Immersive learning experiences (virtual labs, interactive simulations)
+    -   Address gaps in engagement and multimodal inputs
+
+3.  **Ethical AI Roadmap** ⚖️
+    -   **For Developers**: Adaptive personalization, plagiarism detection, equitable monetization
+    -   **For Policymakers**: Mandated free tiers, accuracy standards, data privacy protections
+
+---
+
+## 📊 Dataset
+
+The full dataset containing **481,000+ app reviews** from 22 AI educational apps is publicly available on Hugging Face:
+
+**🔗 [GenAI-EdSent Dataset on Hugging Face](https://huggingface.co/datasets/Erfan-Nourbakhsh/GenAI-EdSent)**
 
 The dataset includes:
+-   **App Information**: Metadata for all 22 applications (ratings, descriptions, install counts)
+-   **User Reviews**: Complete review texts with scores, timestamps, and sentiment labels
+-   **Sentiment Analysis Results**: Binary classification outputs from RoBERTa
+-   **Extracted Insights**: Key positive/negative themes per application
+
+### 📁 Repository Data Files
+
+This repository includes curated analysis outputs:
+
+#### 1. **App_Ratings.json** - Application Metadata
+
+Contains Google Play Store ratings and summaries for all 22 apps:
+
+<details>
+<summary>📋 View Sample Data</summary>
 
-- App Information: Metadata for all 22 applications (ratings, descriptions, install counts)
-- User Reviews: Complete review texts with scores, timestamps, and sentiment labels
-- Sentiment Analysis Results: Binary classification outputs from RoBERTa
-- Extracted Insights: Key positive/negative themes per application
+```json
+{
+  "name": "Edu AI - AI Homework Helper",
+  "summary": "AI Homework Helper - Math, Physics, Chemical, etc.",
+  "app_score": 4.55
+},
+{
+  "name": "Answer.AI - Your AI tutor",
+  "summary": "Scan and Get Instant Answers on Your Phone",
+  "app_score": 4.73
+}
+```
 
-## License
+**Top Rated Apps:**
+- 🥇 Studocu: 4.86/5 ⭐
+- 🥈 Gauth: 4.77/5 ⭐
+- 🥉 Kahoot!: 4.74/5 ⭐
 
-Data released under CC-BY, code released under MIT License
+**Lowest Rated Apps:**
+- ⚠️ Teacher AI: 2.29/5 ⭐
+- ⚠️ Blackboard: 3.39/5 ⭐
+- ⚠️ Tutor AI: 3.40/5 ⭐
 
-## Contact
+</details>
+
+#### 2. **Top_5_Points.json** - Sentiment Themes Per App
+
+Contains the top 5 positive and negative points extracted for each application using GPT-4o and GPT-5:
+
+<details>
+<summary>📋 View Sample Analysis (Edu AI)</summary>
+
+**Positive Points:**
+1. ✅ The app enables users to complete homework tasks effectively
+2. ✅ The app delivers perfect-quality assignment outputs that meet high standards
+
+**Negative Points:**
+1. ❌ The app returns answers in Spanish instead of matching the user's preferred language
+2. ❌ Text is displayed in a font size that is too small to read comfortably
+
+</details>
+
+<details>
+<summary>📋 View Sample Analysis (Answer.AI)</summary>
+
+**Positive Points:**
+1. ✅ Reliably helps students complete homework across subjects and grade levels
+2. ✅ Consistently delivers accurate, correct answers
+3. ✅ Explains solutions step by step with reasoning
+4. ✅ Provides answers very quickly with fast response times
+5. ✅ Easy to use with intuitive interface and camera/scan input
+
+**Negative Points:**
+1. ❌ Frequently provides incorrect or incomplete answers (especially math/graphs)
+2. ❌ Core features locked behind expensive paywall
+3. ❌ Restrictive energy/points system limits free usage
+4. ❌ App instability with crashes and broken features
+5. ❌ Unreliable camera/scan and image recognition
+
+</details>
+
+**Key Insights from Theme Analysis:**
+-   **Most Praised**: Homework efficiency, step-by-step explanations, speed
+-   **Most Criticized**: Paywalls, accuracy issues, technical instability
+-   **Category Trends**: Homework helpers excel; LMS/language apps struggle
+
+---
+
+## 🚀 Installation
+
+### Prerequisites
+
+```bash
+Python 3.8+
+pip
+```
+
+### Setup
+
+```bash
+# Clone the repository
+git clone https://github.com/erfan-nourbakhsh/GenAI-EdSent.git
+cd GenAI-EdSent
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### Required Libraries
+
+-   `transformers` (RoBERTa model)
+-   `openai` (GPT API access)
+-   `beautifulsoup4` / `selenium` (web scraping)
+-   `pandas`, `numpy` (data processing)
+-   `matplotlib`, `seaborn` (visualization)
+
+---
+
+## 🔬 Sample Usage
+
+### 1. Sentiment Classification
+
+```python
+# Run RoBERTa-based sentiment analysis
+python Positive_Negative_Points_Classifier.py
+```
+
+### 2. Extract Key Points
+
+```python
+# Use GPT-4o to extract themes from reviews
+python Positive_Negative_Points_Generator.py
+```
+
+### 3. Generate Top Themes
+
+```python
+# Synthesize top 5 positive/negative points with GPT-5
+python Top_5_Points_Generator.py
+```
+
+---
+
+## 📚 Citation
+
+If you use this work in your research, please cite:
+
+```bibtex
+@article{mazaheriyan2025genai,
+  title={Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of E-Teaching},
+  author={Mazaheriyan, Adeleh and Nourbakhsh, Erfan},
+  journal={arXiv preprint},
+  year={2025},
+  url={https://huggingface.co/papers/2512.11934},
+}
+```
+
+---
+
+## 👥 Meet the Researchers
+
+<div align="center">
+
+### 🌟 Behind the Research
+
+*A collaborative effort bridging **educational theory** and **AI technology** to understand how generative AI is reshaping learning experiences worldwide.*
+
+</div>
+
+<table>
+  <tr>
+    <td align="center" width="50%">
+      <img src="https://img.shields.io/badge/Education-Expert-9b59b6?style=for-the-badge" alt="Education Expert"/>
+      <h3>🎓 Adeleh Mazaheriyan</h3>
+      <p><em>Education Researcher</em></p>
+      <p>
+        <strong>Department of Education</strong><br>
+        Islamic Azad University, Isfahan, Iran
+      </p>
+      <p>
+        Specializes in <strong>pedagogical evaluation</strong> and <strong>digital transformation in e-teaching</strong>, bringing educational theory perspective to AI assessment.
+      </p>
+      <p>
+        📧 <a href="mailto:[email protected]">[email protected]</a>
+      </p>
+    </td>
+    <td align="center" width="50%">
+      <img src="https://img.shields.io/badge/AI-Engineer-3498db?style=for-the-badge" alt="AI Engineer"/>
+      <h3>🤖 Erfan Nourbakhsh</h3>
+      <p><em>AI Researcher & Developer</em></p>
+      <p>
+        <strong>Artificial Intelligence Department</strong><br>
+        University of Isfahan, Iran
+      </p>
+      <p>
+        Expert in <strong>NLP</strong>, <strong>sentiment analysis</strong>, and <strong>LLM applications</strong>, developing the technical pipeline for large-scale review analysis.
+      </p>
+      <p>
+        📧 <a href="mailto:[email protected]">[email protected]</a>
+      </p>
+    </td>
+  </tr>
+</table>
+
+<div>
+
+### 🤝 Interdisciplinary Collaboration
+
+This research exemplifies the power of **interdisciplinary collaboration**, combining:
+-   📚 **Educational Theory** → Understanding pedagogical implications
+-   🧠 **AI/ML Technology** → Analyzing 960K+ reviews at scale
+-   📊 **Data Science** → Extracting actionable insights for stakeholders
+
+*Together, we're working to ensure AI in education serves all learners equitably.*
+
+</div>
+
+---
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss proposed changes.
+
+---
+
+## 📄 License
+
+This dataset is licensed under the CC-BY-4.0 License. The code is licensed under the MIT License.
+
+---
+
+## 🌟 Acknowledgments
+
+We extend our gratitude to:
+
+-   **Microsoft Education** for the [2025 AI in Education Report](https://educate.microsoft.com/)
+-   **RAND Corporation** for K-12 AI adoption insights and teacher engagement studies
+-   **OpenAI & Hugging Face** for providing access to state-of-the-art language models (GPT-4o, GPT-5, RoBERTa)
+-   **Google Play Store** developers and the educational AI community
+-   **Thousands of users** whose reviews made this research possible
+
+---
+
+## 📞 Contact & Collaboration
+
+We welcome questions, feedback, and collaboration opportunities!
+
+**For Research Inquiries:**
+-   📧 Email: [email protected] or [email protected]
+-   🤗 Dataset: [Hugging Face](https://huggingface.co/datasets/Erfan-Nourbakhsh/GenAI-EdSent)
+
+**Interested in:**
+-   Collaborative research on AI in education?
+-   Extending this analysis to other platforms (Apple App Store, web apps)?
+-   Developing predictive models for app success?
+-   Contributing to the codebase?
+
+*We'd love to hear from you!* 💬
+
+---
+
+<div align="center">
+
+### ⭐ If you find this research useful, please star the repository! ⭐
+
+[![GitHub stars](https://img.shields.io/github/stars/erfan-nourbakhsh/GenAI-EdSent?style=social)](https://github.com/erfan-nourbakhsh/GenAI-EdSent)
+
+*Advancing equitable, innovative e-teaching through user-driven AI insights*
+
+---
 
-For inquiries, contact [email protected]. 
+Made with ❤️ for the future of education
 
-## Citation
-We will provide our paper soon!
+</div>