Strategies to avoid the simplification of AI interpretation in foreign literature teaching in the digital intelligence era

Abstract

This study aims to explore the causes of AI interpretation simplification and propose targeted teaching strategies to balance technological efficiency and humanistic depth in foreign literature education. By analyzing the limitations of AI training datasets, the convergence of algorithm logic, and teachers’ over-reliance on AI tools, the research constructs a «AI + Teachers-Students» dual-subject interpretation model, designs «technology + humanities» integrated teaching modules, and optimizes the teaching adaptability of AI tools. The findings suggest that these strategies can effectively mitigate the simplification of AI interpretation, enhance students’ critical thinking and literary appreciation abilities, and promote the healthy development of foreign literature teaching in the digital intelligence era. This paper contributes to the theoretical exploration of AI-integrated literary education and offers practical guidelines for front-line foreign language teachers.