Kandjoni Yendouname — BlackSeaShark5 Labs

Works on search-augmented reasoning and learned decoders for scientific problems.

Publications (2)

Jun 2026IJIRTAI

A Large-Scale Observational Study of Generative AI Use, Academic Performance, Skill Retention, and Burnout Risk Among University Students

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

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 demonstrating 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.

Jun 2026IJIRTAI

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

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.