The future of practice: Enabling teachers to create learning interactives with generative UI

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.

The case for active learning

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.

Adapting generative UI for education

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.

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:

1. 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.
2. 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.
3. 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.

Iterative generation with pedagogical guardrails

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.

Generated by AI, vetted by teachers

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.

Our next steps with teachers

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.

Acknowledgements

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.