Rasch Model Analysis of Artificial Intelligence Technology Implementation Among Multidisciplinary FIKK Students in Higher Education - Yudha Munajat Saputra; Enjang Yusuf Ali; Ari Gana Yulianto; Mohd Salleh Aman (Universitas Tadulako, 2026)
The purpose of this study was to analyze the implementation of Artificial Intelligence Technology (AIT) in higher education using the Rasch Model. This study applied a mixed-method approach with a convergent design. The participants were 138 students from a higher education institution in Sumedang, West Java, Indonesia. Data were collected through a 14-item questionnaire distributed via Google Forms. Quantitative data were analyzed using descriptive statistics with the Rasch Model to examine item reliability, person ability, with response patterns. Qualitative data were obtained from open-ended responses to explore students’ experiences with expectations regarding AIT use. The findings showed that AIT had been widely adopted for both academic with non-academic purposes, with 82.6% of students reporting regular use in learning activities. Rasch analysis indicated good instrument reliability (Cronbach’s Alpha = 0.87) with revealed that students perceived AIT as most useful for completing assignments efficiently, while critical evaluation of AI-generated content was the most difficult aspect. Open-ended responses highlighted that students expected AIT to provide more personalized feedback with adaptive learning support. Although internet connectivity remained a challenge, the more distinctive issue was students’ concern about overdependence on AI with reduced independent thinking. It can be concluded that AIT has been substantially integrated into student activities in higher education, but future development should focus on ethical use, critical literacy, with intelligent personalization.