Generative AI as Teacher-Guided Feedback: Exploring Independent Academic Writing among First-Generation Undergraduates in Tamil Nadu

Abstract

First-generation undergraduates in Tamil Nadu often arrive at college with strong oral reasoning but little prior exposure to the conventions of academic English. Many have studied in Tamil-medium schools, and few have a parent or older sibling who can read a draft and say what is missing. Generative AI tools now offer them a reader at any hour. Yet unmediated use can slide into outsourcing, where the tool writes and the student submits. This article examines a middle path: generative AI positioned as teacher-guided feedback rather than as a text producer. Drawing on a qualitative multiple-case design in [two] arts and science colleges in the Kongu region, we followed [n] first-generation undergraduates across a [12]-week writing module. Teachers set explicit boundaries, modelled feedback-seeking prompts, and required students to keep a revision log showing what they accepted, rejected or reworked. Data came from semi-structured interviews, draft histories, revision logs and teacher reflective notes, analysed through reflexive thematic analysis. Four themes emerged: (1) from answer-seeking to questionasking, (2) the teacher as a trust anchor, (3) language as both bridge and barrier, and (4) the slow growth of evaluative judgement. Students who used the tool within a teacher-framed routine reported greater confidence in planning and revising their own arguments, while teacher mediation appeared central to preventing dependency. The study argues that the pedagogical value of generative AI for first-generation writers lies less in the tool than in the feedback culture built around it. Implications are offered for writing instruction, institutional AI policy and bilingual support in regional Indian higher education