AI in Education – Survey-Based Analysis

This project explores how students and teachers from various academic fields perceive the impact of Artificial Intelligence (AI) in education. Based on over 150 survey responses, the analysis aims to uncover key differences in AI adoption attitudes across disciplines like Medicine, Law, and Data Science.

---

Technologies Used

• Python
• pandas – data cleaning & manipulation
• numpy – numerical operations
• matplotlib – data visualization
• scipy – basic statistical analysis

---

📁 Project Structure

AI-in-Education-Analysis/
├── analysis_scripts/              
│   ├── effectiveness_ai_tutors.py
│   ├── support_ai_integration.py
│   ├── ai_grading_accuracy.py
│   ├── impact_learning_experience.py
│   ├── trust_ai_materials.py
│   ├── ai_cheating_prevalence.py
│   ├── age_difference_perception.py
│   └── field_study_difference.py
├── graphs/                       
│   ├── effectiveness_ai_tutors.png
│   ├── support_ai_integration.png
│   ├── ai_grading_accuracy.png
│   ├── impact_learning_experience.png
│   ├── trust_ai_materials.png
│   ├── ai_cheating_prevalence.png
│   ├── age_difference_perception.png
│   └── field_study_difference.png
├── responses.csv                   # Survey data file
├── README.md                       # Project documentation
└── .gitignore

---

📈 Key Insights & Visualizations

1. Effectiveness of AI Tutors vs Human Teachers

2. Support for AI Integration in Teaching

3. AI Grading Accuracy

4. Impact of AI on Learning Experience

5. Trust in AI-Generated Study Materials

6. AI Cheating Prevalence

7. Age Difference in AI Effectiveness Perception

8. Field of Study Difference in AI Acceptance

---

🚀 How to Run

1. Clone the repository
2. Ensure responses.csv is in the root folder
3. Run the individual scripts inside analysis_scripts/
   Example:

   python analysis_scripts/effectiveness_ai_tutors.py

---

🙌 Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a new branch: git checkout -b feature-name
3. Make your changes
4. Commit: git commit -m "Add some feature"
5. Push to your fork: git push origin feature-name
6. Open a pull request 🚀

---

📄 License

This project is licensed under the MIT License – see the LICENSE file for details.

---

📬 Author

Ahmad Sohaib Qasim
BS Data Science, Punjab University
qasim.datadev@gmail.com
GitHub
LinkedIn
Website