Designer Yitian Li is exploring a deceptively difficult question about artificial intelligence: if AI can answer almost anything, how should it be designed to help children keep asking and learning?
That question is at the heart of Quriosity, an AI-powered curiosity engine for children that has earned recognition from international design competitions. The project received a Silver Award in App / Mobile App – Education at the 2026 MUSE Creative Awards and an Honorable Mention in Interactive Design at the 2026 DNA Paris Design Awards.
The recognition places Quriosity within globally competitive design programs. The MUSE Creative Awards reports more than 100,000 entries from 124 countries across its history, supported by a network of 200+ international judges and criteria including originality, execution, effectiveness, strategic thinking, and impact. DNA Paris similarly evaluates work through an international jury of designers, editors, and creative professionals, attracting nearly 3,000 entries from more than 70 countries annually, with approximately the top 10% receiving recognition.
For Li, however, Quriosity began not with technology, but with a behavioral problem: children’s curiosity happens spontaneously, while most ways of finding information require them to enter a structured learning or a one-off search experience. When a question cannot be explored in the moment, the impulse behind it can quickly disappear.
Rather than approaching the challenge as another educational app, Li began by studying how curiosity actually emerges. The team conducted qualitative interviews with 12 parent-child groups, ages 6 to 15, examining curiosity triggers, question-asking behaviors, existing tools, and what makes a discovery meaningful.
The research revealed an important distinction: providing information is not necessarily the same as sustaining curiosity. Search engines and general-purpose AI can retrieve answers almost instantly. Li became more interested in what happens after that first answer: whether a system could help children build deeper understanding while continuing to make new connections.
That insight shaped Quriosity as a “curiosity engine” instead of simply an answer look-up. Everyday objects become starting points for exploration, while conversational AI and personalized prompts help children move through related ideas. The technology deliberately recedes into the background; the experience is organized around the child’s evolving curiosity rather than the capabilities of AI itself.
Research also substantially changed the product. After developing low-fidelity concepts, Li tested them with seven additional groups of parents and children, leading to four significant changes to the interaction model and product strategy. One challenge became especially clear: children’s thinking can branch remarkably quickly. A single answer may immediately trigger questions about several new subjects. AI makes those transitions effortless, but unrestricted question-and-answer exchanges do not necessarily help children form a coherent body of knowledge.
Li responded by balancing depth with exploration. Quriosity’s deep-dive journey encourages children to examine multiple dimensions of the same subject before earning a reward, while Mystery Cards channel their instinct to branch outward by introducing related discoveries within a thematic structure. Together, these mechanisms transformed the concept from an open-ended AI conversation into a more intentional learning loop. As Li describes it, “The goal was to give curiosity structure without taking away its spontaneity.”
The approach reflects Li’s broader philosophy toward emerging technology. With a background spanning architecture, information science, and human-computer interaction, alongside professional experience designing AI and agentic experiences at Google, she approaches products as interconnected behavioral systems rather than collections of features. That systems thinking is particularly important when designing AI for children. The system must be informative without overwhelming, personalized without prescribing what a child should learn, and capable enough to assist without allowing technology to dominate the experience.
For Li, this means designing not only the interface around AI but also the behavior of AI itself. Considerable attention in Quriosity went into its Model UX and system instructions, determining how the intelligence should organize information, guide exploration, and encourage a constructive next action. “AI can provide the information, but design has to shape what happens next,” Li says. That principle gives Quriosity relevance beyond children’s education. At a time when generative AI products are often measured by how quickly and completely they can answer questions, Li proposes another measure of success: whether intelligent systems can reinforce human curiosity, agency, and positive patterns of behavior.
Quriosity’s recognition by both the MUSE Creative Awards and DNA Paris Design Awards underscores the strength of that approach. More importantly, the project demonstrates Li’s ability to move beyond the novelty of AI itself: to identify a nuanced human behavior, investigate it through research, and translate those insights into an original, cohesive product system. It is this combination of systems thinking, human-centered research, and emerging-technology expertise that distinguishes Li’s work as a designer, and positions Quriosity as a compelling example of what thoughtful AI design can achieve.
Edited by Ben VanderVeen · About Moss & Fog
