<![CDATA[While Artificial Intelligence (AI) disrupts translation, Arabic-to-Indonesian literary translation remains challenging as AI systems fail to achieve pragmatic equivalence amidst complex religious, cultural, and rhetorical nuances. This qualitative case study at UIN Maulana Malik Ibrahim Malang investigates these challenges and students' roles as cultural mediators. Involving 10 Arabic literature students, data were gathered via phenomenological interviews and comparative analysis of Arabic literary texts, raw outputs from four AI platforms (ChatGPT, DeepL, Google Translate, Gemini), and students' post-edited versions, using Mona Baker’s framework. Findings reveal AI produces literal translations devoid of cultural and spiritual context. To counteract this cultural distortion, students employ a tripartite mediation strategy: identifying implicit source-text implicatures, evaluating target readers high-context backgrounds, and executing creative post-editing. The study concludes the translator's role has evolved into a curator of cultural meaning, making post-editing an indispensable 'second creative process' requiring human hermeneutic sensitivity.]]>
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