Comparative Study: Millennials' and Gen Z's Perceptions of Using DeepL for Arabic Learning

Abstract

This study aims to comparatively analyze the perceptions of Millennials and Generation Z regarding the use of DeepL as a tool for learning Arabic at Islamic universities. Employing a qualitative approach with a phenomenological design, data were collected through in-depth interviews, with validity ensured using source, technique, and time triangulation. The data were analyzed using the Miles and Huberman model to identify patterns of differences and similarities in DeepL utilization. The findings indicate that Millennials rely more on conventional learning methods, using DeepL primarily as a supplementary tool for translation and vocabulary comprehension. They prefer guidance from lecturers, classical books, or recordings of native speakers to enhance their phonology, morphology, and syntax skills. In contrast, Generation Z is more technologically adaptive, integrating DeepL with additional applications such as the Arabic Morphology Analyzer and online forums to deepen their understanding. They tend to be more skeptical of AI accuracy and are more proactive in exploring various digital resources. This study contributes theoretically by reinforcing Marc Prensky’s (2001) digital native theory, Piaget’s (1972) constructivism theory, and Bandura’s (1986) social learning theory in the context of AI-based Arabic language learning. However, this study is limited in scope, as it was conducted only at UIN Walisongo Semarang. Therefore, further research should be conducted at various other Islamic universities to obtain a broader understanding of AI usage patterns in Arabic language learning.