<![CDATA[Theological concepts, particularly those concerning the nature and attributes of God, are often abstract and conceptually complex, requiring systematic and reflective forms of reasoning. This study examines students’ perceptions of applying Computational Thinking (CT) to the understanding of fundamental theological concepts and investigates whether these perceptions differ by gender. A quantitative cross-sectional descriptive design was employed. Data were collected from 46 undergraduate students (23 female and 23 male) selected through purposive sampling based on their prior exposure to Islamic Religious Education and fundamental theological concepts. Data were obtained using a 12-item Likert-type questionnaire covering six CT dimensions: decomposition, abstraction, pattern recognition, algorithmic thinking, systems modeling and simulation, and data structuring. Descriptive statistics were used to characterize students’ perceptions, while the Shapiro–Wilk test was employed to assess distributional assumptions, and an independent-samples t-test was used to examine gender differences. The overall mean perception score was 77.40 on a 100-point scale, indicating a relatively favorable perception of applying CT to theological learning. Female students obtained a slightly higher mean score than male students (77.71 vs. 77.07); however, the difference was not statistically significant (p > 0.05). These findings suggest that, within the present sample, students’ perceptions of CT-informed theological learning were generally favorable and did not differ significantly by gender. The study contributes to the emerging discussion of CT as a transdisciplinary cognitive framework that may complement, rather than replace, interpretive and reflective approaches in theological education. Further research using larger samples, validated multidimensional instruments, and intervention-based or mixed-methods designs is needed to determine whether CT-integrated instruction improves students’ actual theological understanding and analytical performance.]]>
