Artificial Intelligence (AI) Tools in Higher Education: An Empirical Study of Student Usage Patterns, Dependency and Ethical Perceptions

IJCSE
International Journal of Computer Science Engineering Techniques
ISSN 2455-135X Β· Peer-Reviewed Β· Open Access
πŸ“š Volume 10, Issue 3
πŸ“… June 13, 2026
πŸ“„ Pages 141–148
πŸ”– ID: IJCSE-V10I3P20

Artificial Intelligence (AI) Tools in Higher Education: An Empirical Study of Student Usage Patterns, Dependency and Ethical Perceptions

Author(s)

Gaurav Naik, Ronit Pednekar, Masoom Shaikh, Usman Khan

Abstract

This study empirically examines the prevalence, purposes, and ethical perceptions of Artificial Intelligence (AI) tool utilization among higher education students. Design/Methodology: A quantitative, cross-sectional descriptive survey was administered to 120 students spanning undergraduate and postgraduate levels across multiple academic disciplines. The collected data was analyzed using descriptive statistics and three Chi-Square Tests of Independence at a significance level of Ξ± = 0.05. Findings: The study found that ChatGPT (80.0%), Google Gemini (68.3%), and Claude (56.7%) are the dominant platforms. A significant relationship was confirmed between a student’s field of study and their dependency level (Ο‡ 2 = 18.42, p = 0.0306), between usage frequency and perception of reduced creativity (Ο‡ 2 = 21.55, p = 0.0003), and between daily time spent and verification behavior (Ο‡ 2 = 13.21, p = 0.0398). A critical verification gap was identified: only 27.5% of students consistently verify AI-generated content, while 60.8% have submitted AI-generated text with minimal editing.

Keywords

Artificial Intelligence, AI literacy, student dependency, ChatGPT, academic integrity, verification gap, higher education

References

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πŸ“‹ How to Cite This Paper

Gaurav Naik, Ronit Pednekar, Masoom Shaikh, Usman Khan (2026). Artificial Intelligence (AI) Tools in Higher Education: An Empirical Study of Student Usage Patterns, Dependency and Ethical Perceptions. International Journal of Computer Science Engineering Techniques, 10(3), 141–148. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.20683366
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