We had the distinct pleasure to interview award winning designer Yongwen Dai about their original and creative Knowledge Mapping Platform design work. Yongwen Dai is an award-winning communication designer, that makes our world a better place with their original knowledge mapping platform works.
Yongwen Dai ("YD") answered Design Interview ("DI") questions about their award-winning work Hive AI Knowledge Mapping Platform. Interview questions prepared by Frank Scott.
YD : Hive AI is driven by the belief that learning is nonlinear and deeply personal. Traditional AI learning tools often impose rigid structures, limiting curiosity and autonomy. Hive AI challenges that by creating a system where learners build their own knowledge networks, guided by AI as a facilitator, not an instructor. Our inspiration comes from how neural networks process information—dynamic, flexible, and constantly evolving.
YD : Our primary focus was to empower learners to take ownership of their knowledge journeys. We wanted to create an environment where users could explore ideas organically, make unexpected connections, and visualize their learning in ways that resonate with them. The goal was to make learning both structured and exploratory, blending AI capabilities with human curiosity.
YD : We plan to expand Hive AI’s capabilities—introducing more customizable visualization templates, refining AI-driven recommendations, and integrating with other learning platforms like research databases and online courses. We also aim to make Hive AI accessible to a wider audience by developing multilingual support and mobile-friendly interfaces.
YD : The concept evolved over approximately six months, including ideation, prototyping, user research, and refinement. It’s a product of iterative design cycles and ongoing exploration into AI-driven learning systems.
YD : Hive AI was born from inspiration, not commission. We saw a gap in the way AI was being applied in education—often limited to static Q&A systems. We wanted to explore a new paradigm where AI supports exploratory learning, helping users build knowledge structures tailored to their interests.
YD : We intend to develop and maintain Hive AI as an independent project, but we are open to collaborations with educational institutions, research organizations, and learning platforms that align with our vision.
YD : We are passionate about learning and knowledge systems. This project allowed us to combine our interests in UX design, AI, and education to create a tool that helps people think more deeply, not just memorize facts.
YD : We were inspired by systems thinking pioneers like Donella Meadows, and visualization tools like concept maps and knowledge graphs. While Hive AI is unique in its approach, we stand on the shoulders of many who have explored how people process and organize information.
YD : Our target users are learners—students, professionals, and researchers—who deal with complex, interdisciplinary information. Hive AI supports anyone who wants to connect ideas across fields and build a personalized understanding of their subjects.
YD : Hive AI breaks away from linear learning models by enabling non-linear, node-based exploration. Its unique hexagon knowledge nodes, AI-driven gap detection, and customizable visualizations empower users to build, expand, and reorganize their knowledge in ways that feel intuitive and engaging.
YD : The name "Hive" symbolizes a dynamic, collaborative space where knowledge grows organically—like a beehive. It reflects the interconnectedness of ideas and the continuous, evolving nature of learning.
YD : We used Figma for interface design, Miro for collaborative mapping, and Notion for documentation. For the AI components, we worked with Python frameworks like PyTorch and TensorFlow, and used D3.js for dynamic visualizations.
YD : The most unique feature is the hexagon knowledge node system combined with AI-driven knowledge gap detection. This allows learners to see their knowledge as an interconnected web rather than isolated facts—supporting deep understanding and discovery.
YD : Yes, Hive AI was a collaborative effort between UX designers, AI engineers, educators, and researchers. We worked closely with machine learning experts to ensure the AI recommendations aligned with human cognitive patterns, and with educators to refine the learning experience.
YD : Technology is the enabler—AI powers the recommendations and visualizations, while the interface design ensures usability and engagement. Hive AI wouldn’t exist without advanced machine learning algorithms and interactive visualization tools, but its value lies in how it supports human curiosity.
YD : Absolutely. We conducted field and secondary research on learning sciences, cognitive load theory, and user behaviors in digital learning environments. We also ran user interviews and prototype tests to understand how learners navigate complex topics and what kinds of visualizations aid their understanding.
YD : Balancing complexity and clarity was a major challenge. We wanted to support deep, interconnected learning without overwhelming users. Ensuring that the AI recommendations felt helpful rather than intrusive, and designing intuitive visualizations for abstract concepts, required multiple iterations and close user feedback.
YD : We believed Hive AI represented an innovative approach to AI-assisted learning, and we wanted to share our vision with a broader community. Submitting to A' Design Award was a way to gain feedback, visibility, and inspiration from peers in the design world.
YD : We deepened our understanding of interdisciplinary design—how AI, education, and UX intersect. We also learned the importance of designing for ambiguity: creating systems that adapt to users' evolving needs, rather than forcing them into rigid paths.
YD : Hive AI is more than a tool—it’s a mindset shift. It encourages learners to see knowledge as a living system they can shape, not just passively consume. We hope Hive AI inspires new conversations about the future of learning and the role of design in supporting curiosity.
