Abstract:

The integration of AI into the teaching of literature presents both risks and challenges that demand critical examination. While AI tools can assist in text analysis, facilitate access to resources, and personalize learning experiences, their use in literary education raises questions about interpretation, creativity, and ethics. In this paper, I will go through some of these questions, using examples from my own teaching of literature in classes of French as a foreign language in a Swedish university.

To start with, literary studies are grounded in ambiguity, multiplicity of meaning, and cultural context. AI systems, however, operate on algorithms that prioritize pattern recognition and statistical correlations. This can lead to interpretations that are mechanistic, privileging surface-level features such as word frequency or sentiment analysis over deeper thematic, historical, and philosophical dimensions. Consequently, there is a danger that students may come to view literature as a set of quantifiable data points rather than as a complex humanistic discourse.

In my examples, I tried to raise the students’ awareness of this danger, by letting them analyze AI generated literary analyses of works that the students have read themselves and studied during the classes.

Secondly, the ability of AI to generate essays, summaries, and even creative writing poses a significant challenge to academic integrity. When students rely on AI-generated content, the development of critical thinking, interpretive skills, and original argumentation—the core objectives of literary education—may be compromised. This raises pedagogical questions about assessment: How can teachers ensure that student work reflects genuine engagement rather than algorithmic output? Traditional plagiarism detection tools are insufficient in this context, necessitating new strategies for fostering authentic learning.

In my own examples, I tried to reduce these risks by reading the literary works in several parts, and discussing the with the students in small groups, on the basis of literary theory that was not distributed to them in advance, bur rather discovered deductively during these discussions.

Thirdly, AI systems are trained on large datasets that often reflect dominant cultural narratives and systemic biases. In literature teaching, this can result in the marginalization of minority voices or the reinforcement of Eurocentric perspectives. For example, an AI trained primarily on Western literary criticism may struggle to interpret texts from non-Western traditions or postcolonial frameworks.

I tried to transform this issue into a discussion point, by letting the students analyze a AI generated essay on a novel by a postcolonial author.

Lastly, perhaps the most fundamental challenge lies in maintaining the dialogic nature of literary education. Literature is not merely a subject to be analyzed; it is a space for conversation, empathy, and shared meaning-making. Overreliance on AI risks reducing this human-centered process to an automated transaction. Using AI as a supportive tool should not mean allowing it to supplant the interpretive dialogue that defines the discipline.

As a conclusion, it could be said that the use of AI in literature teaching should not be seen as only detrimental, but it requires a critical, reflective approach. The challenge is to ensure that technology serves as an aid to interpretation rather than a substitute for the interpretive act itself.

Keywords:

Technology, AI, Literature.