A Study on Self-Regulated Learning Strategies in AI-Assisted Reading Preparation: Evidence from the Content and Language Integrated Course Comprehensive English: British Literary Works

Abstract

In flipped classrooms under the Content and Language Integration (CLI) framework, English majors’ self-regulated learning (SRL) ability plays a crucial role in the quality of pre-class learning. However, in traditional flipped classrooms without the support of generative artificial intelligence (GenAI), SRL feedback is often delayed and lacks personalization, which restricts the development of students’ goal setting, learning monitoring, and strategy regulation. To explore the empowering mechanism of GenAI in SRL, this study draws on Zimmerman’s social-cognitive model of SRL and Pintrich’s classification system of SRL strategies. Six English-major students were selected through purposive sampling to participate in two rounds of AI-assisted preview practice. By analyzing their AI interaction texts, reflective journals, and interview data, this study qualitatively reveals the influence of AI on the use of cognitive, metacognitive, and resource management strategies across the three phases of forethought, performance, and reflection. The findings show that metacognitive monitoring and help-seeking strategies were significantly activated, while critical thinking, time management, and organizational strategies were moderately used, whereas deep-processing and collaborative strategies were used to a limited extent. Through immediate feedback and external scaffolding, GenAI supported learners’ goal setting, task decomposition, and self-evaluation, but still faced challenges in stimulating deep cognitive processing and learning motivation. The findings provide a theoretical foundation and practical path for integrating GenAI into foreign language teaching.