Anchored to the text, owned by the student: a policy & practice review for generative AI in literature education
Abstract
The rapid uptake of generative AI in schools collides with a changing ecology of reading characterized by multimodality, fragmented attention, and increased reliance on short-form content. Literature education therefore faces a dual challenge: sustaining close reading and interpretive depth while teaching students to use AI responsibly. This Policy & Practice Review offers a practice-oriented framework that links AI literacy (capabilities, limits, responsibilities) to literary literacy through an evidence-first routine: TASU (Text → AI → Student → Teacher). We contribute (1) a policy-oriented set of classroom rules that operationalize bounded AI use for literary learning (disclosure, evidence-first sequencing, authorship protection, privacy safeguards, teacher-led judgment, and gradual scaffold reduction); (2) a taxonomy of seven didactic functions for AI in literature teaching (reading companion, Socratic questioner, writing studio, curator, differentiation, assessment support, creative partner), each aligned to core literary goals and paired with low-tech alternatives; (3) ready-to-teach mini-models across levels (primary to upper secondary), including the Revise–Locate–Justify (RLJ) routine that strengthens evidence placement and warrants while protecting student voice; and (4) assessment and ethics guardrails that standardize line/page citation and lightweight process artefacts (evidence ledger, warrants list, rejection log) to stabilize validity in AI-present classrooms. The framework is designed for classroom adaptation and iterative improvement. The framework is designed for classroom adaptation and iterative refinement rather than as a claim of causal effectiveness; its purpose is to specify implementable guidelines and monitoring points that reduce common risks (hallucinated quotations, authorship displacement, bias, privacy breaches, and over-reliance) while preserving the human-centred practices of literature education: interpretation, argument, and reflective authorship.
Fields of Study & Subjects
Artificial Intelligence in Healthcare and Education
Computational and Text Analysis Methods
Hate Speech and Cyberbullying Detection
Publication details
- Journal / source
- Frontiers in Education
- Publisher
- Frontiers Media SA
- Volume
- 11
- Publication date
- 29 April 2026
- Language
- EN