Scholarlink

<p>This longitudinal study, grounded in Dynamic Systems Theory (DST), explores how language teachers’ integration of AI tools evolves over an 18-week period, revealing AI adoption as a complex pedagogical transformation rather than a simple technological shift. Drawing on these findings, the research introduces two models: (1) the DST-informed Pedagogical Content Knowledge (PCK) model, which specifies AI-empowered PCK by detailing the five domains of knowledge teachers draw upon, and how these inform teaching practice and scaffolding strategies for personalised student learning; and (2) the AI-in-PCK stage framework, which maps the trajectory of AI adoption, illustrating how teachers’ concerns and practices evolve from initial exploration and experimentation to strategic integration and ongoing learning, while responding to classroom realities and student feedback. Together, these models illuminate adaptive, multifaceted changes in PCK and teaching practice, highlighting how AI integration shapes decision-making and professional growth. The findings underscore critical implications for designing flexible, context-responsive professional learning and systemic support strategies, particularly in under-resourced rural contexts, and provide a foundation for future AI-in-PCK research.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An empirical longitudinal study of AI integration in transforming teachers’ pedagogical content knowledge: insights from language educators in rural China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月25日

查看全文
  • Gretchen Geng,
  • Junyu Chen

摘要

This longitudinal study, grounded in Dynamic Systems Theory (DST), explores how language teachers’ integration of AI tools evolves over an 18-week period, revealing AI adoption as a complex pedagogical transformation rather than a simple technological shift. Drawing on these findings, the research introduces two models: (1) the DST-informed Pedagogical Content Knowledge (PCK) model, which specifies AI-empowered PCK by detailing the five domains of knowledge teachers draw upon, and how these inform teaching practice and scaffolding strategies for personalised student learning; and (2) the AI-in-PCK stage framework, which maps the trajectory of AI adoption, illustrating how teachers’ concerns and practices evolve from initial exploration and experimentation to strategic integration and ongoing learning, while responding to classroom realities and student feedback. Together, these models illuminate adaptive, multifaceted changes in PCK and teaching practice, highlighting how AI integration shapes decision-making and professional growth. The findings underscore critical implications for designing flexible, context-responsive professional learning and systemic support strategies, particularly in under-resourced rural contexts, and provide a foundation for future AI-in-PCK research.