<![CDATA[Artificial intelligence (AI) is increasingly used in case-based learning, yet its implications for students’ analytical reasoning remain insufficiently examined. Previous studies have emphasized learning efficiency, user perception, and general performance outcomes, while less attention has been given to whether AI-mediated responses reflect independent analytical development or textual alignment with assessment structures. This study examined differences in students’ pedagogical analysis across unmediated and AI-mediated response conditions in early childhood teacher education. A one-group pretest-posttest design was employed involving 60 second-semester students in an Early Childhood Islamic Education Study Program. Data were collected through case-based essay tasks on child language development and assessed using an analytical rubric covering problem identification, theoretical use, language development analysis, impact analysis, and pedagogical recommendation. The posttest condition involved AI support and a mandatory prompt closely aligned with the rubric dimensions. Descriptive statistics and the Wilcoxon Signed-Rank Test were used to analyze score differences, while selected responses from cases of non-improvement or decline were examined to interpret possible changes in contextual reasoning. The mean score increased from 14.25 to 16.50 (Z = -6.280; p < .001; r = .81). Although 49 students improved, 7 showed no change, and 4 experienced score declines. These findings indicate that AI-mediated and prompt-structured conditions may support more organized written responses, but they do not necessarily demonstrate independently internalized analytical capacity. Selected response comparisons suggest that technical coherence may coexist with weakened contextual caution. The study highlights the limitation of product-based written assessment and the need for process-oriented assessment.]]>
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