Abstract

Social media has become an integral part of student life, influencing communication, collaboration, and learning behaviors. While it provides valuable opportunities for academic engagement, excessive use can lead to distractions and diminished academic outcomes. This study examines the impact of Social Media Usage (SMU) on Student Academic Performance (SAP), with a focus on how demographic factors such as gender, department, and program type shape social media behavior. It also investigates the application of statistical and machine learning techniques to predict academic outcomes and develop AI-driven dashboards for identifying at-risk students. A structured questionnaire was distributed to 150 students from selected colleges, resulting in 135 responses. After validating the data for completeness and accuracy, 80 responses were retained for analysis. Using a Simple Random Sampling (SRS) method ensured unbiased representation of participants. The collected data comprised demographic variables, social media usage patterns, and academic performance indicators. Correlation analysis was used to identify relationships between variables, ANOVA examined demographic influences, and linear regression assessed predictive effects. Additionally, machine learning methods were integrated to improve predictive accuracy and reveal hidden patterns in the data. The findings reveal that SMU has a significant and measurable impact on SAP, with both positive and negative effects depending on usage patterns. Demographic factors such as gender, department, and program type significantly moderated the relationship between social media engagement and academic performance. Moreover, the AI-driven dashboards effectively identified students at risk of underperformance, offering educators a visual and actionable tool to design targeted interventions. This study provides valuable insights for students and educators seeking to strike a balance between social media engagement and academic success. By combining traditional statistical methods with machine learning and AI-based visualization, the research demonstrates a comprehensive and practical framework for understanding and managing the complex interplay between social media behavior and academic performance. These insights can inform institutional strategies aimed at promoting responsible social media use and supporting at-risk students in higher education.