# Google 如何用生成式 UI 让教师创建学习互动内容

- 来源：Google Research：Blog（网页）
- 发布时间：2026-09-18 05:18
- AIHOT 分数：37
- AIHOT 链接：https://aihot.news/items/cmu617lg303k1rofj5i4y9sbr
- 原文链接：https://research.google/blog/the-future-of-practice-enabling-teachers-to-create-learning-interactives-with-generative-ui

## AI 摘要

Google 基于 LearnLM 的学习科学原则，让教师主导提出主题并生成可修改、需教师批准的学习目标，再据此生成游戏化学习互动内容。每个互动按目标拆分为难度递进的关卡，并配有 AI 生成的脚手架提示、分层提示、针对性反馈和完整解答，而非直接给出答案。

## 正文

Instructional design

We first sought to determine what good, interactive learning experiences look like. We drew on established learning science to define a number of key pedagogical principles, aligning with those behind the development of LearnLM:

Aligning with a curriculum and teacher-approved learning objectives (e.g., for earth science, comparing how varying degrees of cloud cover influence local temperature and predicting how wind speed and direction affect weather patterns).

Promoting active, inquiry-based learning, which requires both motivation and guidance to be effective.

Ensuring each simulation is factuality accurate.

These principles come to life in our game-based learning design. To encourage motivation, each learning interactive features a series of progressively difficult challenges, based on the learning objectives (e.g., in the earth science example mentioned above, the first level focuses on the temperature, before progressing to harder challenges about rapid warming and storms). This is combined with a suite of scaffolded hints, instructions and feedback (e.g., directing the learner to the relevant formula or explaining a specific term) to provide each individual learner with the support they need to complete each level.

We define generation requirements to include:

Careful articulation of learning objectives: By design, the educator is in the lead and suggests the topic they want to focus on. We then generate a set of precise and coherent learning objectives. These are modifiable and can be tailored to suit the curriculum goals. They must be approved by the teacher and they serve as the basis for the generation of the learning interactives.

Structured game levels: We build upon elements of game-based learning and break each complex topic into levels with clear goals aligned with the learning objectives. The students explore the topic through a series of progressively difficult challenges (see the progression of levels at the top of the visual below). This promotes active experimentation and sustains learner motivation by pairing deliberate practice with calibrated challenge, fostering a growing sense of competence as students gain proficiency in increasingly complex concepts.

AI generated scaffolding: In order to effectively guide learners through the levels, we generate a suite of scaffolded guidance. Shown below, this includes an introduction to prime a student’s prior knowledge, a toolbox with relevant formulas and theories, multiple levels of hints, tailored feedback reflecting on why a specific response is working or not working, and worked solutions to strengthen comprehension after the student’s own exploration. These were all tested and iterated upon with teachers and students. By providing real-time, context-aware guidance, rather than simply revealing the answers, we encourage critical thinking and help students figure out the solutions for themselves.