Many language programs are debating whether and how to use AI-assisted feedback in writing instruction as these tools become common in higher education. This talk reports the preliminary phase of a larger project examining the pre-adoption landscape of AI feedback on student writing at an English-medium instruction university. We focus on students' informal AI practices before curricular integration, faculty beliefs, and institutional uncertainty. Guided by sociocultural theory, we use a qualitative-dominant mixed-methods design: semi-structured faculty interviews, institutional document analysis, and student surveys on out-of-class AI use for writing and feedback. Interview and document data are analyzed thematically and through discourse analysis; survey data are summarized with descriptive statistics. Preliminary results suggest distinct faculty stance profiles, widespread but uneven student use of AI-generated comments outside instruction, and significant discrepancies between the instructor's goals and students' actions. The study shows how AI becomes a contested mediating tool before formal guidelines are established and provides CALL researchers and practitioners with empirically grounded insight into the circumstances, conflicts, and moral issues that influence AI adoption, informing more feasible and pedagogically sound AI-mediated feedback interventions.