人工智能赋能高中英语词汇教学的实践路径与边界反思

Exploring Practical Pathways and Pedagogical Boundaries in AI-Empowered High School English Vocabulary Teaching

摘要: 在高中英语教学中，词汇学习既是语言能力发展的基础，也是学生阅读理解、书面表达和跨文化交际的支撑。传统词汇教学中存在机械记忆效率不高、语境运用不足、学情差异难以兼顾、反馈评价滞后等问题。人工智能技术为词汇诊断、复习推送、语境生成、输出反馈和教学决策提供了新的支持，但其价值并不在于替代教师，而在于促进词汇教学由“统一讲授–重复背诵”转向“诊断–建构–运用–反馈” 的学习闭环。本文在梳理国内外人工智能辅助词汇教学研究的基础上，结合建构主义和联结主义学习理论，分析Kimi、文心一言等生成式人工智能工具的课堂应用案例，从智能诊断、语境化输入、个性化复习、输出反馈和数据改进五个方面分析人工智能赋能高中英语词汇教学的实践路径，并进一步讨论课堂实施中应关注的技术依赖、数据安全、资源差异和教师专业判断等问题。研究认为，人工智能只有嵌入真实课堂任务并接受教师的教学选择、语料筛选和价值引导，才能切实提高词汇学习的精准性、参与度和迁移效果。

Abstract: In senior high school English teaching, vocabulary learning is not only the foundation of language competence development, but also an important support for students’ reading comprehension, written expression, and intercultural communication. Traditional vocabulary instruction is often constrained by inefficient rote memorization, insufficient contextualized use, limited attention to learner differences, and delayed feedback and assessment. Artificial intelligence provides new support for vocabulary diagnosis, review recommendation, contextualized input generation, output feedback, and instructional decision-making. Its value, however, lies not in replacing teachers, but in facilitating a shift from the traditional model of “uniform instruction and repeated memorization” to a learning cycle of “diagnosis, construction, application, and feedback.” Based on a review of domestic and international research on AI-assisted vocabulary instruction, and drawing on constructivist and connectionist learning theories, this paper analyzes classroom application cases of generative AI tools such as Kimi and ERNIE Bot. It explores practical pathways for empowering senior high school English vocabulary teaching through artificial intelligence from five dimensions: intelligent diagnosis, contextualized input, personalized review, output feedback, and data-informed improvement. The paper further discusses key issues that should be considered in classroom implementation, including technological dependence, data security, unequal access to resources, and teachers’ professional judgment. The study argues that only when artificial intelligence is embedded in authentic classroom tasks and guided by teachers’ pedagogical decisions, corpus selection, and value orientation can it effectively enhance the precision, engagement, and transferability of vocabulary learning.