The rapid advancement of Artificial Intelligence (AI) offers transformative potential for language education. This paper investigates the integration of AI technologies with a layered contextualization approach within an experiential teaching model to enhance junior high school English speaking instruction. Addressing the limitations of traditional one-size-fits-all pedagogy, this study proposes a framework where AI tools (intelligent voice evaluation, adaptive learning platforms, natural language processing) are leveraged to create dynamic, authentic interactive learning environments, paired with stratified instructional design tailoring tasks to students’ beginner, intermediate and advanced proficiency levels. Grounded in Constructivism, Situated Learning Theory and Blended Learning models, this paper analyzes the theoretical underpinnings and practical implementation of this integrated approach, while identifying a research gap in combined AI and layered experiential application in middle school EFL contexts. It details AI’s roles in personalized learning, real-time feedback and immersive scenario simulation, plus implementation strategies for layered contextualized tasks. The proposed model shows significant potential to improve oral proficiency, learner autonomy, critical thinking and classroom dynamics. This paper concludes with pedagogical implications, implementation challenges and directions for future empirical research.

Wang Qiong. Integrating AI and Layered Contextualization in an Experiential Teaching Model for Junior High School English Speaking. Frontiers in Educational Research (2026), Vol. 9, Issue 3: 61-67. https://doi.org/10.25236/FER.2026.090309.

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