Integrating AI into the literature classroom can foster engagement by inviting students to reflect on the nature of textual interpretation and meaning-making. This poster introduces the dialogic interpretation framework, which positions AI as a conversational provocation rather than an interpretative authority. In this model, AI acts as one voice among many, prompting students to develop evidence-based arguments about interpretation. Using Kafka’s The Metamorphosis and NotebookLM AI tool as a case study, the poster demonstrates how this framework can foster critical inquiry. The process invites students into a reciprocal dialogue in which they treat AI-generated interpretations as springboards for more nuanced textual exploration. Through active engagement with AI and their peers, students analyze AI’s interpretations, noting ideas, patterns, and questions. Then using structured reflection and collaboration, students examine AI’s interpretative limitations, such as its grasp of tone, irony, and emotional nuance. Identifying these gaps cultivates interpretative agency and confidence, enabling students to treat AI like a conversational partner while relying more readily on their own analytical skills and judgment. This presentation discusses strategies for designing activities and prompts, offers concrete examples, and considers broader implications for teaching literature in the age of generative AI. By foregrounding reflective and interpretative habits rather than replacing human analysis with technology, this approach emphasizes AI as a tool to revitalize curiosity and critical thinking in the study of literature.

Authors:
Rachel Smydra, Oakland University, United States

About the Presenter(s)
Dr. Rachel Smydra is an Associate Professor of English at Oakland University, Rochester, Michigan. Much of my scholarly work has focused on embedding active learning strategies into the classroom to stimulate curiosity and knowledge acquisition.

See this presentation on the full schedule – Monday Schedule

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