人工智能赋能的多模态高中英语阅读教学——语篇深度解读的路径与策略

AI-Empowered Multimodal High School English Reading Instruction—Paths and Strategies for In-Depth Discourse Interpretation

摘要: 高中英语阅读课堂长期存在一个结构性矛盾。教师习惯于围绕文本语言点展开精细讲解，学生则满足于获取表层信息、完成答题任务，语篇本身蕴含的文化意涵、篇章逻辑和思辨空间反而被边缘化。人工智能与多模态教学理念的引入为突破这一困局带来了新的想象空间，但技术究竟以何种方式介入阅读教学才能真正服务于语篇的深度解读，这仍然是一个亟待澄清的问题。本文以多模态话语分析理论为依托，结合人工智能的技术特征，尝试从语篇深度解读的核心诉求出发，系统梳理AI赋能多模态阅读教学的实践可能性。文章首先回应当前高中英语阅读教学在语篇处理上的现实偏差及其成因，继而顺着四条线索展开论述。多模态资源的情境化生成可以创设感知条件，语篇结构的可视化解析有助于把握整体脉络，人机协同的思辨对话能推动思维深化，基于学情数据的精准干预则为个性化指导提供了可能。对这些路径的实施策略，文中辅以教学实例加以说明。本文主张，AI的价值不在于替代教师完成解读，而在于拓展语篇解读的感知维度和交互深度，让阅读教学真正从“教学生读懂文本”走向“教学生读透文本”。

Abstract: A structural contradiction has long existed in senior high school English reading classrooms. Teachers tend to deliver meticulous instruction centered on textual language points, while students are content with acquiring surface-level information and completing test-oriented tasks. As a result, the cultural connotations, discourse logic, and space for critical thinking embedded in the text itself are progressively marginalized. The introduction of artificial intelligence and multimodal teaching concepts has opened up new possibilities for breaking this deadlock. However, the question of how technology should be integrated into reading instruction to genuinely serve the deep interpretation of discourse remains an urgent issue to be clarified. Grounded in multimodal discourse analysis theory and informed by the technological features of AI, this paper attempts to systematically examine the practical possibilities of AI-empowered multimodal reading instruction, starting from the core demands of deep discourse interpretation. The article first addresses the current deviations in discourse processing within senior high school English reading teaching and their underlying causes, and then proceeds along four interconnected lines of inquiry. The contextual generation of multimodal resources can create conditions for perceptual engagement; the visualized analysis of discourse structure facilitates the grasp of overall textual organization; human-machine collaborative dialogic interaction promotes deeper thinking; and data-based precise intervention offers the potential for personalized guidance. Teaching examples are provided to illustrate the implementation strategies of these approaches. This paper argues that the value of AI lies not in replacing teachers’ interpretive work, but in expanding the perceptual dimensions and interactive depth of discourse interpretation, thereby transforming reading instruction from merely “teaching students to read the text” to genuinely “teaching students to read through the text.”