Assessing the Level of AI Literacy among Muslim University Students: A Rasch Model Analysis
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Abstract
The modern world is experiencing a high rate of development and widespread adoption of Artificial Intelligence (AI) in daily activities. However, the emergence of generative Artificial Intelligence applications such as ChatGPT has brought much public and pedagogical debate. Consequently, the rapid implementation of generative AI requires more careful and important analyses of its real potential and its consequences in relation to educational practice. Therefore, AI literacy is an important skill that must be possessed, especially by Muslim students in Indonesia, because there is increasing support for the use of digital and AI-based technologies in Indonesia universities. Moreover, one aspect of AI literacy, which is ethics in line with the teachings of Islam, namely itqan (excellence and responsibility) and amanah (trustworthiness). AI literacy is broader than the ability to use AI technically; it also requires a critical and comprehensive understanding of the ramifications surrounding the use of AI. Thus, given the importance of AI literacy and in line with the concepts taught by Islam, using the Rasch model analysis, this research aims to investigate the level of AI literacy among Muslim Indonesian University students. Employing non-experimental quantitative research with convenience sampling, a total of 286 Muslim students participated in this study. The data was collected through an online questionnaire and analyzed using Winsteps 3.75 version, a Rasch model analysis software, to assess students’ AI literacy level. The findings indicated that students, in general, exhibited moderately high scores on the majority of AI literacy dimensions, and the profile of the students was generally adaptive to AI technology. Furthermore, because this study provides evidence of students’ AI literacy, this study could be a useful source to help universities design more adaptive and ethically-oriented curricula, and policymakers come up with measures that facilitate value-based and inclusive AI learning.
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References
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