1 Introduction

In recent years, AGI technologies such as DeepSeek have fostered a human-machine symbiotic knowledge-learning ecosystem, reshaping students' cognitive development pathways. AI integrates into students' cognitive systems in college English reading and writing instruction, reconstructing knowledge construction, language expression, and socialization processes. Research must shift from tool application to deeper exploration of learning cognitive system structures and mechanisms. This study constructs an integrative theoretical framework to systematically elucidate key dimensions and practical pathways for AI-empowered literacy cognitive development, promoting the fusion of technology-enabled methods with socio-cognitive practices and clarifying the connotation of meta-cognitive development under AI intervention.

2 Origin of the Problem

This section examines the role and dialectical nature of AI in English literacy cognitive development, noting that AI can expand students' disciplinary horizons, deepen critical thinking levels, and enhance emotional exploration capabilities. However, over-reliance on AI may lead to cognitive alienation, hindering deep learning. The generalized and decontextualized outputs of AI often render its "standard answers" mismatched with specific contexts. Thus, instructional design should cultivate students' contextual awareness, guiding them to calibrate and reconstruct AI outputs within authentic contexts. Existing research predominantly focuses on binary human-AI interactions without situating them within broader socio-cultural practices, whereas literacy development also involves participating in discourse communities, mastering genre conventions, and engaging in socio-cognitive practices. English literacy instruction in China has long faced challenges of insufficient social interaction opportunities, easily falling into templated and test-oriented dilemmas. While current AI-enabled models can enhance critical thinking and language skills, how to help students deeply engage in discourse community practices remains underexplored. When students reflect, evaluate, and plan with AI assistance, the connotation of meta-cognition shifts. Traditional meta-cognition often involves implicit mental activities, whereas AI dialogues externalize internal reflection as prompt-based commands, making cognitive processes visible and operable. While this improves efficiency, it raises a critical pedagogical dilemma: Can students critically scrutinize AI-generated content and ultimately develop independent, autonomous meta-cognition? Action research reveals students' desire to become independent cognitive agents, marking the initial萌芽 of autonomous meta-cognitive development. Therefore, guiding students to transition from AI-mediated meta-cognition to autonomous meta-cognition is key to addressing current teaching challenges. Methodologically, existing research on students' reflective AI use remains conceptually ambiguous, necessitating clear distinctions between task-efficiency-oriented AI-assisted regulation and fully autonomous meta-cognitive activities as independent cognitive agents.

3 Framework Construction

This chapter constructs a teaching and research framework for AI-empowered English literacy cognitive development, grounded in Vygotsky's sociocultural theory and Piaget's cognitive development theory, to explore AI's role in cognitive growth. The framework addresses AI's dual effects of cognitive advancement and alienation, expanding cognitive space through personalized genre tasks and dynamic scaffolding while fostering deep reflection and self-regulation to cultivate adaptive能力. AI-enabled cognitive development must be embedded in authentic socio-cognitive practices, with genre pedagogy offering a critical pathway. Tardy et al. identify six constructs of genre knowledge development, emphasizing its dynamic evolutionary nature. Meta-cognition plays a pivotal role in transforming genre competence from cognition to application. This study proposes "AI-independent meta-cognition," stressing learners' need to re-internalize and autonomously control cognitive cycles—particularly in evaluating and deciding among AI-generated strategic options—thus deepening the traditional self-regulated learning model's "autonomous control" dimension. Self-determination theory posits that fostering "AI-independent meta-cognition" responds not only to skill development but also to learners' innate need for autonomy, serving as the psychological foundation for resisting technological over-mediation and preserving learner agency.

The framework comprises three construct clusters: 1) The dialectical relationship between AI empowerment and cognitive alienation, requiring teachers to balance these forces to promote cognitive growth; 2) Genre-task-driven recontextualization for meta-cognitive development, using AI-designed personalized genre tasks to facilitate recontextualization; 3) AI-independent meta-cognition, encompassing meta-cognitive knowledge and regulation, demanding students transcend AI frameworks to form independent thinking while managing psychological pressures, transforming cognitive conflicts into upgraded thinking for autonomous development.

This integrative theoretical framework merges core principles of meta-cognition, knowledge development, cognitive psychology, and genre pedagogy to address fundamental requirements of English literacy cognitive development in human-AI symbiosis. It embeds AI-enabled cognitive construction within socio-cognitive practices of human-machine collaboration, where AI's辅助 functions become meaningful cognitive scaffolds. The socio-cognitive dimension thus bridges internal cognitive construction and external socio-cultural worlds, ensuring students' cognitive activities are not abstract exercises but embodied applications tested and refined in real-world tasks. The framework ultimately aims to cultivate "independent yet socially engaged cognitive agents," with theoretically coherent and practically interlocking components forming an organic whole.

4 Teaching and Research Implications

This section discusses AI's applications in English literacy instruction and its implications for teaching and research. Pedagogically, the framework emphasizes the core pathway of "cognitive construction—socio-cognitive practice—meta-cognitive development," guiding college English reading and writing curricula. AI mitigates mismatches between student proficiency and text complexity, aiding comprehension of textual structures, authorial arguments, and rhetorical strategies while connecting socio-cultural background knowledge. Instructional design should prioritize "recontextualization," requiring students to perform cognitive tasks with AI assistance before independently negotiating meaning and making judgments to advance reading meta-cognition. In writing instruction, AI helps students overcome language barriers by providing targeted suggestions and examples, while students autonomously decide how to integrate them. Lessons must incorporate "disengagement points," where students rewrite or revise drafts independently post-AI assistance to cultivate AI-independent meta-cognition.

For research, the framework inspires action research or evidence-based teaching改进. Teachers can conduct micro-process case studies, collecting AI dialogue logs, draft texts, and reflection journals to diagnose cognitive challenges and progress in human-AI collaboration. Comparative studies of task variables can explore how different scaffolding methods affect meta-cognitive development. Longitudinal documentation of student growth helps assess pedagogical efficacy and understand individualized cognitive pathways. Such contextualized evidence from frontline teaching can inform practice and enrich understandings of AI-era literacy cognitive development.

5 Conclusion

This paper underscores the significance of the AI-empowered English literacy cognitive development framework for fostering students' cognitive independence and active social participation. The framework provides theoretical guidance and practical pathways for AI applications in English literacy instruction while reaffirming the student-centered essence of education amid technological empowerment. Teachers face implementation challenges requiring突破 traditional pedagogic mindsets to balance AI empowerment and cognitive alienation, demanding subject-matter expertise, technological proficiency, and meta-cognitive pedagogical awareness. Professional development should focus on deep integration of technology, cognition, and teaching, strengthening辩证 thinking, contextual design, and meta-cognitive coaching. Though grounded in established theories and practices, the framework awaits large-scale empirical validation, necessitating future research. Safeguarding learner autonomy while promoting advanced cognition and social adaptability remains central to English literacy education—this framework aims to provide theoretical and practical support for achieving these goals.