Artificial Intelligence Exposure and Human Mental Growth Trajectories: A Multiclass Predictive Analysis of Cognitive, Emotional, and Wellbeing Outcomes

Ranveer Singh, Aryan Somanna, Sidhesh Mishra, Kandjoni Yendouname, Riduvarshini M, Shrey Jauhari, Venkatanathan O R V, Trushti Patel

Published: 18 June 2026
Venue: IJIRT
DOI: —

Abstract

The proliferation of artificial intelligence (AI) tools across professional, educational, and social domains has prompted urgent scholarly inquiry into their long-term effects on human cognitive and psychological development. This study investigates whether measurable dimensions of AI engagement — including usage intensity, dependency score, tool proficiency, and scenario context — combined with individual cognitive and emotional attributes, can predict discrete mental growth trajectories across a global population spanning five decades (2024–2075). Using a dataset of 52,000 records across nine regions, five demographic cohorts, and seven AI developmental eras, we train and evaluate four multiclass classifiers: Logistic Regression, Random Forest, XGBoost, and LightGBM. All models achieve remarkably high performance, with Logistic Regression attaining the highest validation accuracy of 99.71%, macro-averaged F1 of 0.9973, and ROC-AUC of 0.9999 on the held-out test set. SHAP-based explainability analysis identifies Mental Wellbeing Score, Social Interaction Quality, and Emotional Intelligence as the dominant predictors of outcome class membership. These findings suggest that psychological and social dimensions of AI exposure are more strongly associated with mental growth trajectories than raw usage metrics. Implications for AI policy, digital mental health interventions, and human-centered AI design are discussed.