ChatGPT as Learning Support among Mechatronics Engineering Technology Students: A Descriptive Survey

Abstract

Generative artificial intelligence is increasingly used in higher education, yet evidence from applied engineering programs remains limited. This descriptive survey examined self-reported ChatGPT use across six academic dimensions among 51 first- and third-semester students enrolled in an Applied Bachelor program in Mechatronics Engineering Technology who had previously used ChatGPT. A 30-item, five-point Likert questionnaire assessed frequency of use, theory, report writing, coding, design, and usage behavior. Descriptive statistics and Cronbach's alpha were calculated. Overall use was moderate (M = 3.270). Theory had the highest mean (M = 3.490), followed by design (M = 3.290), frequency of use (M = 3.255), coding (M = 3.243), reports (M = 3.180), and usage behavior (M = 3.161). The full instrument yielded alpha = 0.923, whereas Reports (alpha = 0.581) and Usage Behavior (alpha = 0.450) showed weak internal consistency. The findings indicate that ChatGPT primarily supported conceptual learning; broader interpretation requires caution.