AI is changing how students access information, explore ideas, and engage with learning. But as AI becomes increasingly integrated into education, an important question is emerging: How do we ensure that AI complements the learning experience rather than replacing it?
This question was at the center of the 2026 Tools Competition’s call for AI-Enabled tools to support curiosity in science learning, supported by the Gordon and Betty Moore Foundation. This priority invited competitors to explore how AI could support youth-driven inquiry and help learners investigate authentic questions about the world around them.
This question is especially important in science learning, which is about asking questions, investigating phenomena, testing ideas, interpreting evidence, and revising understanding when new information emerges. However, these same skills are important in all domains, helping learners explore ideas, solve problems, and think critically across different areas of learning and in everyday life.
This post builds on a recent Tools Competition webinar about AI, curiosity, and deeper thinking in science learning. It highlights three 2026 winners that demonstrate different approaches to supporting curiosity, from hands-on exploration to feedback and learning beyond the classroom.
Defining the Role of AI in Learning
One of the biggest opportunities and challenges for AI in education is defining its role in the learning process.
AI can provide explanations, summarize information, and generate responses almost instantly. But when technology moves students too quickly toward an answer, it can unintentionally remove some of the experiences where deeper learning happens: the uncertainty, experimentation, reflection, and problem-solving that help students build understanding.
As Janet Coffey from the Moore Foundation shared during the webinar, the goal is not to create learning experiences where AI replaces curiosity or becomes the primary driver of learning. Instead, the strongest AI-enabled tools can position technology as a support that helps learners notice patterns, generate questions, compare ideas, interpret evidence, and reflect on their own thinking.
This reflects a broader understanding of how students learn science. Learners develop scientific thinking through active exploration, experimentation, reflection, and problem-solving, rather than simply receiving information or answers. AI can support these experiences by creating more opportunities for learners to explore ideas, work through challenges, and connect what they are learning to the world around them, while fostering curiosity and deeper thinking.
Supporting More Meaningful Hands-On Science Learning
Hands-on learning can give students opportunities to explore scientific ideas in ways that are active, tangible, and connected to the world around them. But hands-on activities do not automatically lead to inquiry-driven learning. If students are simply following directions to reach an expected result, they may have fewer opportunities to make decisions, test their own ideas, and pursue questions that emerge along the way.
FUNKE SENSE Lab, a 2026 Tools Competition winner, combines hands-on science kits with augmented reality and AI-supported guidance to create more interactive and accessible science learning experiences for learners across Kenya and beyond. The project is also designed with inclusive learning in mind, including support for neurodiverse learners and students with different learning needs.
Rather than presenting science as a set of concepts to memorize, FUNKE SENSE Lab encourages students to predict, experiment, observe outcomes, reflect on what happened, and continue asking questions. AI supports the experience, but students remain at the center of the exploration and discovery.
Supporting Deeper Thinking Through Feedback and Reflection
Another opportunity for AI is helping learners navigate complex problems by providing guidance and feedback at the moment it is needed.
NoRILLA, a 2026 Tools Competition winner, explores how AI can enhance hands-on STEM learning through mixed reality experiences. The platform combines physical experimentation with adaptive AI support, allowing students to build, test, and revise their ideas while receiving feedback tailored to their learning process.
For example, when students design structures to withstand an earthquake simulation, the goal is not simply to determine whether their structure succeeds or fails. Instead, the AI helps students think through why something happened and what they might change in their next attempt.
Tools like NoRILLA demonstrate how AI can support the thinking process itself by encouraging students to analyze, reflect, and improve.
Extending Curiosity Beyond the Classroom
Curiosity does not end when students leave the classroom. Informal learning environments, including homes, libraries, museums, community programs, and other spaces outside of school, can give learners opportunities to explore questions that connect to their everyday lives and interests. These settings can also offer more flexibility for learners to pursue a question, try something new, and return to an idea over time, often with family members, mentors, or peers involved in the experience.
Lab-on-a-Book explores how AI can extend hands-on science inquiry into informal learning environments. The project pairs a physical science book, which includes hands-on experiments and materials embedded in its pages, with an AI companion that supports students as they explore.
The AI is designed to scaffold inquiry rather than provide shortcuts or simply tell students the correct answer. As students work through experiments, it can encourage them to try different materials, test new approaches, and explore “what if” questions that emerge from their investigations.
Lab-on-a-Book demonstrates how AI can help make informal science learning more engaging and inquiry-driven. A student might bring an experiment to the kitchen table and explore materials they encounter at home, using the AI to help pursue questions that arise along the way. In this way, AI can help connect science learning to the world students experience outside of school.
Designing for Curiosity, Not Just Efficiency
As conversations about AI in education continue, it is easy to focus on efficiency, including faster feedback, quicker answers, or streamlined tasks. While these applications have value, they represent only one part of AI’s potential.
The examples highlighted through the 2026 Tools Competition demonstrate another possibility: AI can complement the human elements that make learning meaningful. Teachers, mentors, peers, hands-on experiences, and real-world connections remain essential.
The opportunity lies in using AI to design experiences that help students remain curious longer, engage more deeply with evidence, and explore questions that matter to them.
The winning tools focused on science curiosity offer a glimpse of what this future can look like: AI not as a replacement for curiosity, but as a tool that helps nurture it.
