Between Efficiency and Dependency: Empirical Patterns of Generative Artificial Intelligence Use Among University Students
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Abstract
The rapid integration of Generative Artificial Intelligence (GAI) in higher education has transformed learning dynamics and academic production. This study aimed to qualitatively analyze the usage patterns of GAI tools among engineering undergraduate students at a private university in Montería, Colombia, identifying their implications for critical thinking, academic autonomy, and educational integrity. A qualitative exploratory-descriptive design was employed, based on non-participant direct classroom observation during an academic term (2026-I), recording the interactions of 45 students across quantitative and project management courses. Data were collected via field diaries and analyzed through qualitative thematic analysis. The findings revealed three dominant patterns: substitution of procedural effort in quantitative tasks, standardization of written outputs, and emerging practices of methodological concealment using prompts designed to emulate human errors to evade detection. It is concluded that while GAI optimizes processing time, its uncritical use leads to operational dependency that weakens cognitive engagement, highlighting the urgent need for authentic assessment strategies and algorithmic literacy.
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