AI Impact on Students Dataset

The AI Impact on Students Dataset provides a comprehensive view of how AI usage influences academic outcomes, study habits, mental health, and burnout risk. With data from 50,000 students, the dataset enables researchers, educators, policymakers, and data scientists to explore both the opportunities and challenges associated with AI adoption in education.

Dataset Overview

The AI Impact on Students Dataset captures student behavior, academic performance, AI usage patterns, institutional policies, and well-being indicators. The dataset contains 50,000 records and 16 features, offering a balanced and complete resource for educational analytics and machine learning applications.

Dataset Highlights

• Total Students: 50,000
• Features: 16
• Format: CSV
• Missing Values: None
• Domain: Education, Artificial Intelligence, Student Well-being
• Target Variables:
  • Post Semester GPA
  • Skill Retention Score
  • Burnout Risk Level

Key Features

Academic Profile

The dataset includes academic information such as:

• Major Category
• Year of Study
• Pre-Semester GPA
• Post-Semester GPA

These features help evaluate how AI usage may impact academic performance over time.

AI Usage Behavior

Several attributes capture how students interact with generative AI tools:

• Weekly GenAI Usage Hours
• Primary Use Case
• Prompt Engineering Skill
• Tool Diversity
• Paid Subscription Status

This information enables analysis of AI adoption patterns and learning outcomes.

Study Habits

The dataset includes study-related metrics such as:

• Traditional Study Hours
• Perceived AI Dependency

These variables provide insight into how students balance AI-assisted learning with conventional study methods.

Mental Health and Well-being

To support educational and psychological research, the dataset contains:

• Anxiety Level During Exams
• Skill Retention Score
• Burnout Risk Level

These features help explore the relationship between AI usage and student well-being.

Institutional Context

The dataset also considers the role of educational policies through:

• Allowed With Citation
• Strictly Banned
• Actively Encouraged

This allows researchers to compare outcomes across different institutional approaches to AI.

Applications

Academic Performance Analysis

Study how AI usage correlates with changes in GPA and academic achievement.

Burnout Prediction

Develop machine learning models that predict student burnout risk based on AI habits, study patterns, and anxiety levels.

Skill Retention Research

Analyze whether reliance on AI tools affects long-term knowledge retention and learning effectiveness.

Educational Policy Evaluation

Compare student outcomes under different institutional AI policies and regulations.

Mental Health Analytics

Explore relationships between AI dependency, exam anxiety, and overall student well-being.

Machine Learning Projects

The dataset supports:

• Classification models
• Regression analysis
• Predictive analytics
• Educational data mining
• Student behavior analysis

Benefits for AI and Education Research

The dataset offers several advantages:

• Large-scale student population
• No missing values
• Balanced representation across majors and study levels
• Academic and psychological metrics in one dataset
• Multiple target variables for machine learning
• Ready-to-use format for analysis and modeling

Conclusion

The AI Impact on Students Dataset offers valuable insights into the growing role of artificial intelligence in education. By combining academic performance data, AI usage behaviors, study habits, institutional policies, and well-being indicators, the dataset enables comprehensive research into how AI is shaping student experiences. With 50,000 student records and multiple machine learning targets, it serves as a powerful resource for educational analytics, policy evaluation, student success research, and AI-driven learning studies.