Reading With, Not Through, AI: A Human-Centered Framework for Multimodal Literature Education
DOI:
https://doi.org/10.37497/rev.artif.intell.educ.v7ii.1377Keywords:
Generative Artificial Intelligence (GAI), Generative AI, Multimodal AI, Literature education, AI literacy, Close reading , Student agency, Visual hallucinationAbstract
Background: Multimodal generative artificial intelligence (GenAI) can process written language, images, typography, layout, and interface-like elements, creating opportunities for literature education but also new epistemic risks. Vision-capable models may combine accurate recognition, plausible inference, and invented visual detail in a single fluent interpretation. The central challenge is not access to multimodal information, but disciplined separation of observation, inference, interpretation, and human judgment.
Objective: This perspective paper develops Multimodal Evidence-Bounded Interpretation (MEBI), a human-centered framework designed to preserve close reading, interpretive plurality, evidential accountability, and student authorship when multimodal GenAI is used with literary texts.
Methods: The study uses an evidence-informed conceptual synthesis and a worked multimodal case. Recent peer-reviewed research on GenAI-supported learning, student agency, literary analysis, multimodal AI, and visual hallucination is integrated with established scholarship on multimodality and graphic narrative and with human-centered AI guidance. The framework is demonstrated through selected scan locations from Nora Dåsnes’s Czech graphic narrative Na kočičí svědomí (2024).
Results: MEBI comprises five design propositions—human-first encounter, modal traceability, observation–inference separation, preserved interpretive plurality, and human adjudication and authorship—and a five-phase sequence: Encounter, Describe, Hypothesize, Audit, and Adjudicate & Author. Five evidence tags track verbal, image, sequential-spatial, typographic-graphic, and digital-interface evidence. A worked case, classroom sequence, evidence ledger, and assessment heuristic operationalize the framework.
Conclusion: MEBI positions AI as a generator of descriptions, hypotheses, counter-readings, and questions rather than as interpretive authority. It offers a researchable design for multimodal literature education while requiring empirical validation across genres, languages, age groups, and educational contexts.
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