内容来源：https://research.google/blog/the-future-of-practice-enabling-teachers-to-create-learning-interactives-with-generative-ui/

内容总结：

谷歌研究团队近日发布了一项新研究成果，探索如何利用生成式用户界面（GenUI）技术，让教师能够为不同主题和学生动态生成引导式互动模拟教学工具，从而推动主动学习。该研究由谷歌研究科学家Gal Elidan和产品经理Yael Haramaty主导，相关实验已获得初步教师正面反馈。

研究团队指出，尽管数字教育工具已深刻改变学生获取信息的方式，但当前数字学习仍以被动体验为主。互动性强、多模态的练习形式虽有助于学生主动思考和解决问题，却因制作成本高、数量有限且对教师投入要求大而难以普及。为此，谷歌将生成式UI技术优化应用于教育场景，使AI模型能够动态构建用户界面，而非依赖预先编码。

基于学习科学中的主动学习原则，研究团队定义了多项关键教学原则，并将其融入游戏化学习设计中。每个学习互动工具围绕学习目标设置逐级递进的挑战，并配备分层提示、说明和反馈，为不同学习者提供个性化支持。生成过程中还引入自校正循环机制，通过教学性、机制和视觉等多维度评估，确保内容质量。评估过程具有代理性，例如通过自动打开Chrome实例模拟用户操作，甚至尝试极端对抗性操作，直至生成结果满足全部标准。

目前，谷歌已发布一个包含30多个英语STEM学科学习互动工具的样本库，涵盖物理、化学、生物和数学，主要面向初高中。这些工具均由AI生成并经教师审核。使用Google Workspace for Education的学校可通过Google for Education试点项目报名提供反馈。英国STEM教师的评估结果显示，整体评分达到良好或优秀，其中物理和化学学科最适合模拟生成。美国12名教师参与的初步研究中，每位教师请求了三个定制互动工具，平均质量评分为8分（满分10分）。教师普遍认为，动态生成解决了长期存在的课堂难题——无法利用静态现成模拟实现差异化教学。有教师表示，分层提示和逐步解答模拟了他们个别辅导学生时的引导方式，等级进阶也与真实评估和练习题目高度契合。

研究团队强调，教师始终是课堂的核心，技术应服务于教育者和其教学目标。所有已发布的学习互动工具均经教师审核批准，涵盖开普勒行星运动定律、数据可视化和抛体运动等课程主题。未来，谷歌将与Google for Education合作，在全球学校开展试点，教师可申请为任何定制STEM概念生成模拟，经教师验证批准后方可纳入公共库。同时，团队还将开展用户体验研究和实地研究，评估学习效果和学生参与度。

中文翻译：

2026年9月17日
Gal Elidan，研究科学家；Yael Haramaty，产品经理，Google Research

我们探索如何利用生成式UI与学习设计护栏，让教师能够为每个主题和每位学生生成引导式互动模拟。

数字教育工具已经改变了全球学生获取信息的方式，从在线教科书到视频资源库，不一而足。然而，尽管技术和可及性取得了显著飞跃，数字学习往往仍给人一种被动体验的感觉。互动性强、引人投入的多模态练习形式能够鼓励学生独立思考、自主探索解题路径，在学习方面具有巨大潜力，却仍然在很大程度上难以普及。这类内容制作成本高昂、数量有限，且往往需要教师投入大量额外精力。我们希望看看AI能否帮助弥合这一差距。

今天，我们分享最新研究成果，推动互动学习的前沿发展。我们的新研究实验允许教育工作者创建定制的、互动的、引导式教育模拟。这些学习互动内容根据教师的教学目标和课程量身定制，并通过我们为学习场景优化的生成式用户界面（GenUI）新颖应用动态生成。

在收到来自可信测试者群体的教师初步积极反馈后，我们还发布了一个包含30多个英语学习互动内容的示例库，涵盖STEM学科，包括物理、化学、生物和数学，重点面向初中和高中。这些内容均由AI生成，并经教师审核。使用Google Workspace for Education的学校可以通过Google for Education试点计划报名提供反馈，以改进学习互动内容。该试点是开发更多供公众使用的学习互动内容的早期一步。

学习不是一项旁观者的运动。从教育理论奠基人约翰·杜威1916年提出我们应该“给学生们一些事情去做”，到影响力深远的心理学家让·皮亚杰的开创性研究展示了学习者如何建构知识，人们早已充分认识到，学生通过主动参与能学得更好。现代认知研究，如ICAP框架，证实互动行为始终比被动听讲或阅读能带来更深层的图式建构和更长期的记忆保持。简而言之，学生通过实践来学习。当学生主动实验、验证假设、解决问题时，他们能建立更为完整的心理模型。

主动学习是我们在研究中优化的关键学习科学原则之一。它是LearnLM的基础——这是Google于2024年发布的专为教育微调的生成式AI模型系列，并在2025年的“Learn Your Way”研究实验中得到了探索，该实验利用生成式AI重新构想了经典教科书。在此前研究的基础上，我们着手探索如何利用生成式模型的最新进展来进一步改造内容，帮助教师创建更加主动、更具吸引力的数字学习体验。

为实现这一目标，我们转向了生成式UI——这是一个活跃的研究领域，AI模型动态构建用户界面，而非要求预先编写这些界面。

我们探索了如何为更深层的教育旅程而非快速交互来优化生成式界面。通过精心引导的教学设计和教学法护栏，我们希望赋能教师创建自己的互动环境——量身适配其课程，并根据其情境输入进行调整。

我们首先试图确定什么是优质的互动学习体验。我们借鉴成熟的学习科学，定义了若干关键教学原则，与LearnLM开发背后的原则相一致：

这些原则在我们的游戏化学习设计中得以体现。为激发动力，每个学习互动内容都设有一系列基于学习目标逐步增加难度的挑战（例如，在上述地球科学示例中，第一关聚焦温度，然后进阶到关于快速变暖和风暴的更难挑战）。这与一套脚手架式提示、说明和反馈（例如，引导学习者找到相关公式或解释特定术语）相结合，为每位学习者提供完成每一关所需的支持。

我们定义的生成要求包括：

为确保质量控制，我们在生成过程中内置了自我纠正循环——这意味着这是一个由若干教学法护栏驱动的迭代过程。虽然这增加了生成最终学习互动内容所需的时间，但积极的强化循环确保了更严格地遵守质量标准。这些标准包括教学法（例如，各关卡是否正确覆盖了学习目标并逐步变难？）、机制（例如，按钮是否正常工作？这一关能否被通关？）和视觉方面（例如，界面上是否有冗余物体可能造成干扰？）。在自我纠正循环中，有具有自主性的自动评估过程（例如，可解性评估会打开一个Chrome实例，像用户一样与模拟进行交互）。目标不仅是测试特定解决方案的有效性，还要尝试对抗性操作，如将旋钮调到极端值。自我纠正循环会重复进行，直到生成的结果满足所有要求的标准。

在我们的整个研究中，一个核心指导原则是技术应当服务于教育工作者及其目标。教师是任何课堂的核心，最能理解哪些AI驱动的模拟能吸引学生，以及它们何时何地适合融入课程。今天在库中发布并可用的所有学习互动内容均经教师审查和批准。这些内容包括学校课程中的主题，如开普勒行星运动定律、数据可视化和抛体运动。

此外，一组学习互动内容由英国STEM教师进行了评估。结果显示总体评分为良好或优秀，其中物理和化学最适合创建模拟。完整详情和结果可在我们的技术报告中查阅。

我们还对美国12名教师进行了初步研究。每位教师请求了三个不同的定制互动内容，这些内容针对其特定课堂需求生成。反馈非常积极，教师对互动内容质量的平均评分为8分（满分10分）。教师们强调了动态生成如何解决了一个长期存在的课堂挑战：无法使用静态的、现成的模拟来实现差异化教学。正如一位高中科学教师所解释的：“如果我在教学时能输入这些[针对任何课程主题]，然后就能生成一个模拟，那将太棒了……我一直无法对任何模拟进行差异化调整，因为你就是只能得到现有的那些。”

教育工作者还指出，生成的设计元素与他们的教学目标高度一致：“这就是为什么能够真正制作和构建与教学目标和学习目标完美契合的东西令人兴奋”（初中科学教师）。他们还赞扬了内置的学生脚手架，指出分层提示和详解答案模拟了他们在个别辅导学生时提供的逐步指导，并且关卡进阶与真实的评估和练习题高度对应。

随着我们扩展内容库，仍有许多需要学习和改进之处，我们将与课堂教师合作推进。

我们将与Google for Education合作，在全球各地的学校和课堂中试点学习互动内容。学校可以通过Google for Education试点计划报名参加即将开展的试点，让他们的教师有机会为任何定制STEM概念请求模拟，并根据其课程、学习目标和年级进行量身定制。新生成的学习互动内容将发送给提出请求的教师进行审核。只有在教师验证和批准后，新的学习互动内容才能添加到我们的内容库中供公众使用。

此外，我们将开展用户体验研究和实地研究，以评估在课堂中使用学习互动内容时的学习收益和学生参与度。

通过为学习优化生成式技术，我们离一个学习实践更加主动、有效且为每个时刻量身定制的未来更近了一步。我们感谢教师们在这项持续研究中的合作，并期待构建能够惠及全球学生的学习互动内容。

感谢所有为这项工作做出贡献的人：Alex Moy、Alisa Kovshov、Anisha Choudhury、Anna Iurchenko、Ayça Cakmakli、Ayelet Shasha Evron、Brit Mennuti、Diana Akrong、Femi Olanubi、Ian Li、Ido Lerer、Julia Wilkowski、Lidan Hackmon、Michal Gordon、Nir Kerem、Preeti Singh、Rena Levitt、Rotem Yulzary、Sarah Smith、Shlomi Ben Shimon、Sophie Allweis、Tracey Lee-Joe、Tzvika Stein、Yaniv Carmel、Yishay Mor和Yuri Lev。特别感谢我们的管理层支持者：Niv Efron、Avinatan Hassidim、Maureen Heymans、Amy Keeling、Katherine Chou、Ronit Levavi Morad、Yossi Matias、Chris Phillips和Ben Gomes。

英文来源：

September 17, 2026
Gal Elidan, Research Scientist, and Yael Haramaty, Product Manager, Google Research
We explore how we can harness generative UI with learning design guardrails to give teachers the ability to generate guided, interactive simulations for every topic and student.
Digital educational tools have transformed how students around the world access information, from online textbooks to video libraries. Yet, for all the remarkable leaps in technology and accessibility, digital learning can often feel like a passive experience. Interactive, engaging, multimodal forms of practice that can encourage students to think for themselves and work through solutions have great potential for learning but remain largely out of reach. They are expensive to create, limited in number, and often require a lot more effort from the teacher. We wanted to see if AI could help close this gap.
Today, we’re sharing our latest research which pushes the frontiers of interactive learning. Our new research experiment allows educators to create custom, interactive, and guided educational simulations. These learning interactives are tailored to the teacher’s objectives and curriculum, and are generated dynamically, leveraging a novel application of generative user interfaces (GenUI) that we’ve optimized for learning.
Having received initial positive teacher feedback from a trusted tester pool, we’re also releasing a sample library of over 30 learning interactives in English for STEM subjects including physics, chemistry, biology, and math with a focus on middle and high school. These are all generated by AI and reviewed by teachers. Schools using Google Workspace for Education can sign up to provide feedback to improve learning interactives through the Google for Education Pilot Program. This pilot is an early step toward developing more learning interactives for public use.
Learning is not a spectator sport. From the work of John Dewey, a foundational education theorist, who argued back in 1916 that we should “give the pupils something to do” to that of Jean Piaget, the influential psychologist whose pioneering work showed how learners construct knowledge, it is well established that students learn better through active engagement. Modern cognitive research, such as the ICAP framework, affirms that interactive behaviors consistently yield deeper schema construction and long-term retention than passive listening or reading. In short, students learn by doing. When students actively experiment, test hypotheses, and solve problems, they build a much more complete mental model.
Active learning is one of the key learning science principles that we optimize for in our research. It is fundamental to LearnLM, Google’s family of generative AI models fine-tuned for education released in 2024, and was explored in a 2025 Learn Your Way research experiment that reimagines the classic textbook with generative AI. Building on this earlier research, we set out to explore how the latest advances in generative models could be used to further transform content, helping teachers create digital learning that is much more active and engaging.
To make this possible, we turned to generative UI, an active area of research whereby AI models dynamically construct user interfaces rather than requiring those interfaces to be coded in advance.
We explored how to optimize generative interfaces for deeper educational journeys as opposed to quick interactions. By using carefully guided instructional design and pedagogical guardrails, we want to empower teachers to create their own interactive environments — tailored to their curriculum and adapted to their contextual inputs.
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:
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:
To ensure quality control, we built self-correcting loops into the generation process — meaning that it is an iterative process, driven by a number of pedagogical guardrails. While this increases the time required to generate the final learning interactives, the aggressive reinforcement loop ensures closer adherence to quality criteria. These criteria include pedagogy (e.g., are the levels correctly covering the learning objectives and becoming progressively harder?), the mechanics (e.g., do the buttons work? Can this level be solved?), and visual aspects (e.g., are there redundant objects on the interface that could be distracting?). Within the self correcting loops there are auto evaluation processes that are agentic in nature (e.g., a solvability evaluation opens a Chrome instance and interacts with the simulation as if it were a user.) The goal is not just to test the validity of a specific solution but also to try adversarial actions such as taking knobs to extreme values. The self-correcting loop repeats until the generated outcome meets all of the required criteria.
Throughout our research, a core guiding principle has been that technology should be in service of educators and their goals. The teacher is at the heart of any classroom and is best placed to understand not only which AI-driven simulations would engage their students, but also when and where they fit into the curriculum. All learning interactives released in the library and available today were vetted and approved by teachers. These include topics from school curriculums such as Kepler's Laws of Planetary Motion, Data Visualization and Projectile Motion.
In addition, a collection of learning interactives was evaluated by STEM teachers in the UK. Results show that overall rating is good or excellent with physics and chemistry being the most amenable to simulation creation. Full details and results are available in our tech report.
We also conducted an initial study with 12 teachers in the US. Each of these teachers requested three different custom interactives, which were generated for their specific classroom needs. The feedback was highly positive with an average teacher rating of 8 out of 10 on the interactives’ quality. Teachers highlighted how dynamic generation solves a long-standing classroom challenge: the inability to differentiate instruction using static, off-the-shelf simulations. As one high school science teacher explained, “If I was teaching and I could type this in [for any curriculum topic] and then a simulation would [be generated], that would be amazing... I've never been able to differentiate any of the simulations because it's just, you get what you get“.
Educators also noted how closely the generated design elements aligned with their instructional goals: “That's why this was exciting to actually craft and build something that aligns perfectly with instructional goals and learning objectives” (middle school science teacher). They also praised the built-in-student scaffolding, noting that the tiered hints and worked solutions model the kinds of step-by-step guidance they provide when supporting students individually, and that the level progressions corresponded well to authentic assessment and practice questions.
As we expand the library, there is still much to learn and improve, and we will do so in collaboration with classroom teachers.
In collaboration with Google for Education, we will pilot learning interactives in schools and classrooms around the world. Schools can sign up to join an upcoming pilot through the Google for Education Pilot Program, giving their teachers the opportunity to request simulations for any custom STEM concept tailored to their curriculum, learning goals, and grade level. The newly generated learning interactives will be sent to the teacher who requested them for review. Only after teacher validation and approval can new learning interactives be added to our library and available for public use.
In addition, we will be conducting UX research and field studies to evaluate learning gains and student engagement when using learning interactives in classrooms.
By optimizing generative technologies for learning, we come closer to a future where learning practice is more active, effective, and tailored for every moment. We thank teachers for their partnership with this ongoing research and look forward to building learning interactives that can benefit students around the world.
Shout out to all those who have contributed to this work: Alex Moy, Alisa Kovshov, Anisha Choudhury, Anna Iurchenko, Ayça Cakmakli, Ayelet Shasha Evron, Brit Mennuti, Diana Akrong, Femi Olanubi, Ian Li, Ido Lerer, Julia Wilkowski, Lidan Hackmon, Michal Gordon, Nir Kerem, Preeti Singh, Rena Levitt, Rotem Yulzary, Sarah Smith, Shlomi Ben Shimon, Sophie Allweis, Tracey Lee-Joe, Tzvika Stein, Yaniv Carmel, Yishay Mor, and Yuri Lev. Special thanks to our executive champions: Niv Efron, Avinatan Hassidim, Maureen Heymans, Amy Keeling, Katherine Chou, Ronit Levavi Morad, Yossi Matias, Chris Phillips and Ben Gomes.