The Impact of AI-Driven Personalized Learning on Mathematics Achievement and Student Engagement in Rural vs. Urban Schools in Karnataka, India

# The Impact of AI-Driven Personalized Learning on Mathematics Achievement and Student Engagement in Rural vs. Urban Schools in Karnataka, India

## Introduction
- Overview of AI-driven personalized learning
  - Definition and purpose
  - Importance in modern education
- Research focus
  - Mathematics achievement
  - Student engagement
  - Rural vs. urban school comparison

## Methodology
- Study design
  - Quasi-experimental approach
  - Sample size: 400 students (200 rural, 200 urban)
- Data collection methods
  - Pre-test and post-test assessments
  - Standardized mathematics tests
  - Engagement surveys
  - Classroom observations
  - AI platform analytics

## Results
- Mathematics achievement
  - Experimental group improvements
    - Rural: Pre-test mean 62.5 → Post-test mean 78.3
    - Urban: Pre-test mean 68.2 → Post-test mean 83.9
  - Statistical significance (p < 0.001)
- Student engagement
  - Engagement survey scores
    - Rural experimental group: 78.5 vs. control group: 65.2
    - Urban experimental group: 82.1 vs. control group: 69.8
  - Higher engagement in experimental groups

## Challenges in Implementation
- Rural schools
  - Inadequate internet connectivity (78% vs. 32%, p < 0.001)
  - Lack of devices (65% vs. 28%, p < 0.001)
- Urban schools
  - Fewer infrastructure challenges compared to rural schools

## Discussion
- Key findings
  - Significant improvement in mathematics achievement
  - Enhanced student engagement with AI platforms
- Rural vs. urban disparities
  - Greater implementation challenges in rural areas
  - Need for tailored strategies to address rural challenges

## Recommendations
- Infrastructure development
  - Improving internet connectivity in rural schools
  - Providing necessary devices for students
- Teacher training
  - Expanding professional development programs
  - Equipping teachers to effectively use AI platforms
- Supportive environments
  - Encouraging community involvement
  - Creating policies to support AI integration

## Conclusion
- Summary of the study's impact
  - Positive outcomes of AI-driven personalized learning
  - Importance of addressing rural-specific challenges
- Future research directions
  - Long-term effects of AI platforms
  - Scalability of solutions across regions