摘要
地理大概念层级体系是组织地理单元教学与落实学科核心素养的重要支架,但其提取过程对教师的文献研读和学科理解能力要求较高。本文采用“学科—AI协同”开发模式,开发地理大概念层级体系生成工作台,将其生成过程分解为五个AI生成节点,通过“材料路由—节点生成—教师审阅—反馈修正”的运行机制,把AI生成与教师学科判断相结合,形成有状态、可修改以及可追踪的人机协同过程。本文以“产业区位选择”为例追踪工作台运行的人机协同过程,邀请9位专家对工作台自动生成的7个不同主题的地理大概念层级体系进行独立评议。结果显示,总体均分显著高于“基本达到要求”基线(M=3.71,p<0.01),基本达标率(评分≥3)为92.4%;定向内容分析揭示出核心概念归属、术语规范、层级逐项对应与教学转化是教师反馈的重点。研究表明,该工作台可作为教师提取地理大概念层级体系的辅助支架,但教师自身仍需要具备一定的学科判断。
Abstract
Hierarchical frameworks of big ideas provide an important basis for organizing unit-based geography instruction
and supporting the development of students’geographical competencies. Constructing such frameworks, however, places
substantial demands on teachers’ability to synthesize relevant literature and make discipline-informed judgments. Drawing
on a discipline-informed human–AI co-development approach, this study developed an AI-based workbench for generating
hierarchical frameworks of big ideas in geography. The workbench decomposes the task into fi ve sequential processing nodes
and integrates AI generation with teacher judgment through material routing, node-based generation, teacher review, and
feedback-driven revision. This design creates a human–AI workfl ow in which intermediate outputs can be retained, revised,
and traced. The workbench was fi rst tested on the topic of Industrial Location Choice, with the human–AI revision process
documented in detail. Nine experts then independently evaluated automatically generated frameworks for seven geography
topics. The overall mean rating was signifi cantly higher than the prespecifi ed benchmark of 3, corresponding to“basically
meets the requirements”(M = 3.71, p < .01), and 92.4% of all ratings met or exceeded that benchmark. Directed content
analysis identifi ed four areas in which teacher intervention was most needed: the selection and placement of core concepts,
terminological precision, alignment across hierarchical levels, and connections to teaching practice. The fi ndings suggest that
the workbench can support teachers in constructing hierarchical frameworks of big ideas in geography, but its outputs still
require discipline-informed teacher judgment.
关键词
地理大概念 /
人工智能 /
人机协同 /
地理单元教学
Key words
big ideas in geography /
arti? cial intelligence /
human–AI collaboration /
geography unit teaching
范政彬 尹卫霞 谢子豪.
基于人工智能的地理大概念层级体系生成工作台开发与验证研究[J]. 地理教学. 2026, 0(21): 36-42
Fan Zhengbin, Yin Weixia, Xie Zihao.
Development and Validation of an Artifi cial Intelligence-Based Workbench for Generating
Hierarchical Frameworks of Big Ideas in Geography[J]. Geography Teaching. 2026, 0(21): 36-42
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基金
2025年度北京市数字教育研究课题一般课题“人工智能赋能小学科学教师跨学科教学的实践模型构建与验证研究”(项目编号:BDEC2025619115)
* 本文系第三届地理教学人工智能创新成果评比特等奖作品转化成果。