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

Yuniarmelinda Ikha Meru Brahmanty (Electrical and Mechatronics Engineering Departement, Politeknik Negeri Cilacap, Cilacap, Jawa Tengah, Indonesia)
Arif Ainur Rafiq (Electrical and Mechatronics Engineering Departement, Politeknik Negeri Cilacap, Cilacap, Jawa Tengah, Indonesia)
Feta Kukuh Pambudi (Electrical and Mechatronics Engineering Departement, Politeknik Negeri Cilacap, Cilacap, Jawa Tengah, Indonesia)
Yoana G ita Pradnya Lengari (Mechanical Engineering Departement, Politeknik Negeri Cilacap, Cilacap, Jawa Tengah, Indonesia)

MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering
Vol 8 No 2 (2026): Motivection : Journal of Mechanical, Electrical and Industrial Engineering

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.