AI STUDENT IMPACT

Ranveer Singh, Sidhesh Mishra, Aryan Somanna, Shrey Jauhari, Riduvarshini M, Trushti Patel

Published: 18 June 2026
Venue: IJIRT
DOI: 10.1234/abcd

Abstract

This study investigates the associations between weekly generative artificial intelligence (GenAI) tool usage and three student-level academic outcomes — post-semester GPA,skill retention score, and burnout risk level — using a large cross sectional observational dataset of N = 50,000 undergraduate and graduate students spanning five academic disciplines. Employing covariate-adjusted ordinary least squares regression (ANCOVA), ordered logistic regression, quadratic dose-response modelling, bootstrap mediation analysis, and inverse probability weighting (IPW) with doubly robust correction, we examine whether GenAI tool adoption improves or undermines academic outcomes after controlling for pre-semester GPA, traditional study hours, institutional AI policy, and individual skill characteristics. Bivariate correlation between weekly GenAI hours and post-semester GPA is small and negative (r = −0.019), yet attenuates substantially after adjustment for pre-semester GPA — the dominant predictor (r = 0.927). Perceived AI dependency and examination anxiety partially mediate the GenAI–GPA pathway. Prompt engineering skill level and institutional policy moderate the relationship, with advanced-skill users under permissive policies demonstrat ing attenuated adverse effects. Non-linear modelling reveals an inverted-U pattern with diminishing or adverse returns beyond moderate weekly use thresholds. High-intensity GenAI users report meaningfully elevated exam anxiety (mean 4.99 vs. 3.82 for low users) and 25% higher perceived AI dependency scores. Debugging and troubleshooting use-cases are associated with the strongest GPA gains (∆ = +0.249), while direct-answer generation shows the weakest benefit (∆ = +0.133). These findings carry direct implications for institutional AI governance, curriculum re-design, and student wellbeing programming.