生成式人工智能赋能高中地理情境化教学：内在逻辑、设计路径与实践案例

Generative AI Empower in Contextualized Teaching in High School Geography: Internal Logic, Design Pathways, and Practical Cases

摘要: 教育数字化背景下，针对传统高中地理情境教学素材静态、交互浅层及评价滞后等困境，本文探讨了生成式人工智能(Generative Artificial Intelligence，简称GAI)赋能情境教学的内在逻辑。研究认为，GAI可通过多模态生成重塑情境表征、自然语言交互深化情境体验、动态适配促进素养进阶。据此，本文构建了“课前多模态生成–课中智能交互–课后个性评价”的人机协同教学路径，并以“喀斯特地貌”为例开展案例实践。最后从防范AI幻觉、避免思维浅表化等维度提出优化策略，以期为数智时代地理课堂变革与核心素养落地提供参考。

Abstract: Against the backdrop of educational digitalization, traditional high school geography situational teaching faces dilemmas such as static materials, superficial interaction, and lagging evaluation. This paper explores the internal logic of Generative Artificial Intelligence (GAI) empowering situational teaching. The study suggests that GAI can reshape situational representation through multimodal generation, deepen situational experience via natural language interaction, and promote core competency progression through dynamic adaptation. Accordingly, this paper constructs a human-machine collaborative teaching path of “multimodal generation before class - intelligent interaction during class - personalized evaluation after class,” and conducts a case study using “Karst Landforms.” Finally, optimization strategies are proposed from the dimensions of preventing AI hallucinations and avoiding superficial thinking, aiming to provide references for the transformation of geography classrooms and the implementation of core competencies in the digital intelligence era.