New Pathways for Improving College Students' Reading Literacy in the AI Era

Abstract

The artificial intelligence technology is closely related to reading activity, the features of intelligent text environment include algorithmic recommendation, dialogue-based generation and multimodal feedback; the structure of college students' reading literacy changes from linear text comprehension to attention resource management, multimodal semantic integration, critical interpretation, meta-reading and semantic verification. Analyzing how recommendation algorithms reshape the reading choice and attention structure, the dual effects of dialogue-based generation for comprehension scaffolds and cognitive offloading, regulating interpretive depth and memory integration by multimodal feedback at three levels including the reconstruction of the competence structure, mechanisms of technological effects and improvement pathways, construct pathways such as dynamic matching between adaptive reading tasks and cognitive load, prompt-driven critical reading and semantic verification training, and human-AI collaborative reading reflection portfolios and value-added literacy assessment to theoretically refer to the conceptual renewal of college students' reading literacy and design support systems in the AI era.