ChatGPT Use and Learning Motivation among Informatics Students: A Preliminary Correlational Study

Abstract

Generative AI systems such as ChatGPT are increasingly used by university students for explanation, code inspection, idea generation, and immediate feedback. This preliminary cross-sectional study examines the association between ChatGPT use frequency and learning motivation among 17 Informatics Engineering students at Universitas Mulia, Balikpapan. Data were collected through an online questionnaire using a four-category measure of ChatGPT use frequency and four-point agreement responses for learning motivation. The original analysis reported a Pearson correlation of r = 0.685 with p = 0.002. Using the reported coefficient and sample size, the corresponding test statistic is t(15) = 3.642 and the Fisher-transformed 95% confidence interval is approximately 0.305-0.877, indicating a positive but imprecisely estimated association. The finding is consistent with literature suggesting that responsive AI tools may support engagement and perceived competence, but the correlational design does not establish that ChatGPT use increases motivation. Reverse causality, self-selection, prior achievement, digital confidence, purpose of use, and measurement limitations remain plausible explanations. The study contributes context-specific evidence from informatics students and identifies requirements for stronger follow-up research, including a larger sample, validated motivation measures, clearer characterization of AI use, rank-based sensitivity analysis, and longitudinal or quasi-experimental data.