Classification of Student Profiles Based on Generative AI Dependency and Complex Problem-Solving Capacity: A Machine Learning Approach Using K-Means Clustering (#2413)

Authors

Benites-Rodriguez, Joseph

Salazar-Sandoval, Fredy

Amaro-Guzman, Carlos

Grados-Espinoza, Anna

Torres-Quiroz, Almintor

Narciso-Gomez, Kennedy

Narciso-Huaccho, Yuleissy

Abstract

The rapid integration of Generative Artifi cial Intelligence (GAI) tools, particularly ChatGPT, into higher education has raised concerns about potential cognitive dependency and its impact on students' problem-solving skills. This study aims to classify student profi les based on their dependency on generative AI and their ability to solve complex problems using machine learning clustering techniques. A cross-sectional study was conducted with 847 engineering students from three Latin American universities. The AI Dependency Scale for University Students (EDIAU-15), the Complex Problem-Solving Inventory (IRPC-25), and the ChatGPT Frequency of Use Questionnaire (CFUC) were administered. K-means clustering with Davies-Bouldin optimization identifi ed four distinct student profi les: (1) Critical Autonomists (23.4%, n=198): low AI dependence, high problem-solving ability; (2) Balanced Integrators (31.2%, n=264): moderate AI use, preserved cognitive skills; (3) Dependent Compensators (28.6%, n=242): high dependence on AI, declining problem-solving ability; and (4) Passive Delegators (16.8%, n=143): severe dependence on AI, signifi cantly impaired cognitive functioning. ANOVA revealed signifi cant diff erences between profi les in academic performance (F=47.82, p<.001), metacognitive awareness (F=38.94, p<.001), and self-effi cacy (F=52.17, p<.001). Structural equation modeling confi rmed that AI dependence mediates the relationship between ChatGPT usage frequency and problem-solving ability (β=-0.43, p<.001).