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Academic AI Usage Patterns in India
Education & Learning Analytics
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About
Unique survey data captures the usage of AI tools like ChatGPT, Gemini, and Copilot among 496 Indian college students in their academic lives. Collected in May 2025, this dataset provides insights into student behaviour through 16 attributes, including daily usage, trust in AI, perceived impact on grades, and internet accessibility. It is ideal for educational analytics, machine learning applications, and understanding the evolving role of AI in higher education in India. This dataset is noted as the first of its kind available on Kaggle.
Columns
- Student_Name: The anonymised name of the student.
- College_Name: The name of the college the student attends.
- Stream: The student's academic discipline, such as Engineering or Arts.
- Year_of_Study: The student's current year of study, from 1 to 4.
- AI_Tools_Used: The specific AI tools used by the student (e.g., ChatGPT, Gemini).
- Daily_Usage_Hours: The average number of hours the student spends on AI tools each day.
- Use_Cases: The academic purposes for which AI tools are used, such as for assignments or exam preparation.
- Trust_in_AI_Tools: The student's level of trust in AI tools, rated on a scale of 1 to 5.
- Impact_on_Grades: The perceived effect of AI tool usage on academic grades, on a scale from -3 to +3.
- Do_Professors_Allow_Use: Indicates whether professors permit the use of AI tools (Yes/No).
- Preferred_AI_Tool: The AI tool most preferred by the student.
- Awareness_Level: The student's self-reported awareness of AI, on a scale of 1 to 10.
- Willing_to_Pay_for_Access: Indicates the student's willingness to pay for AI tool access (Yes/No).
- State: The Indian state where the student resides.
- Device_Used: The primary device used to access AI tools (e.g., Laptop, Mobile).
- Internet_Access: The quality of the student's internet connection (Poor/Medium/High).
Distribution
- Format: CSV
- File Size: 497.25 kB
- Structure: The dataset contains 16 columns. The number of rows or records is not explicitly stated in the provided summary, but the file was collected from 496 students.
Usage
- Predictive Modelling: Predict a student's academic performance based on their AI tool usage patterns.
- Sentiment Analysis: Analyse trust levels in AI tools across different academic streams and geographical regions.
- User Segmentation: Cluster students into distinct groups based on their AI usage behaviours.
- Digital Divide Analysis: Study the impact of internet access quality on AI tool adoption and academic outcomes.
Coverage
- Geographic: The data covers students from various states across India, aiming for diverse representation.
- Time Range: The survey was conducted in May 2025. The dataset is expected to be updated quarterly.
- Demographic: The dataset focuses on college students across different years of study (1-4) and academic streams.
License
CC BY-NC-SA 4.0
Who Can Use It
- Education Researchers: To study the integration and impact of AI in modern higher education.
- Data Scientists: For building predictive models related to academic success and student behaviour.
- AI Developers: To understand user preferences and trust levels for improving AI tools.
- Policymakers: To inform decisions related to technology and education infrastructure.
Dataset Name Suggestions
- AI in Indian Higher Education: A 2025 Student Survey
- Indian College Students' AI Tool Adoption and Impact
- Academic AI Usage Patterns in India
- The Role of AI in Indian Academia: Student Perspectives
Attributes
Original Data Source: Academic AI Usage Patterns in India
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