AI in the Classroom: GPT Usage and Learner Typologies in Programming

Abstract

This study examines GPT usage in university-level programming education, focusing on patterns and correlations in students’ learning behaviors. A survey of 438 students from four universities was conducted to analyze their adoption of AI, learning dispositions, and behavioral patterns. The research aimed to understand the current state of GPT adoption and the connection between student learning approaches and their use of these technologies. Findings show that while a majority of students use GPT applications, the frequency and depth of this engagement vary significantly. Students who favor self-directed learning tend to leverage this technology more for personalized learning and self-assessment. Conversely, students more accustomed to traditional teaching methods use it more conservatively. The study identified four distinct learner groups through cluster analysis, each with unique interaction styles. Furthermore, a correlation analysis indicated that learning orientations, such as Technology-Driven Learning and intrinsic motivation, are positively associated with more frequent and effective GPT use.