Trust, Usefulness, and Dependency on AI in Programming: A Hierarchical Clustering Approach

TL;DR

This study investigates how first-year programming students in the Philippines perceive and depend on AI tools, revealing diverse trust levels and usage patterns influenced by infrastructure and exposure, with implications for educational strategies.

Contribution

It introduces a hierarchical clustering approach to categorize student perceptions of AI, highlighting the complex relationship between usage, trust, and usefulness in a developing country context.

Findings

Four distinct student profiles with different trust and usage levels

Dependency on AI remains low due to infrastructural and exposure limitations

High-frequency users do not necessarily trust or find AI more useful

Abstract

While AI tools are transforming programming education, their adoption in underrepresented countries remains insufficiently studied. Understanding students' trust, perceived usefulness, and dependency on AI tools is essential to improving their integration into education. For these purposes, this study surveyed 508 first-year programming students in Pampanga, Philippines and analyzed their perceptions using hierarchical clustering. Results showed four unique student profiles with varying in trust and usage intensity. While students acknowledged AI tools' benefits, dependency remained low due to limited infrastructure and insufficient exposure. High-frequency users did not necessarily report greater trust or usefulness which may indicates a complex relationship between usage patterns and perception. This study recommends that to maximize AI's educational impact, targeted interventions such as infrastructure development, training programs, and curriculum integration are necessary. This study provides empirical insights to support equitable and effective AI adoption in programming education within developing regions.