Hive AI Platform Pemetaan Kawruh by Hive AI is a highly esteemed winner of the very prestigious 2025 A' Design Award Perak in Desain Antarmuka, Interaksi lan Pengalaman Pangguna Competition Category.
PerakAntarmuka· 2025
Hive AI Platform Pemetaan Kawruh
by Hive AI
Client: Hive AI
A' Design Award Perak Winner in Antarmuka Category
Verified Award Winner
Verify Award Status for #169652The Design
About Hive AI Platform Pemetaan Kawruh
Hive AI iku platform pemetaan kawruh kanggo para siswa, profesional, lan peneliti. Platform iki nggunakake simpul kawruh heksagon kanggo nyusun informasi sing kapisah-pisah dadi kawruh sing sesambungan. Pangguna ngunggah teks, video, lan gambar, banjur kecerdasan buatan ngusulake tautan, nglompokake konten sing gegayutan, lan nemtokake bolongan-bolongan kawruh. Navigasi nonlinear ndhukung eksplorasi bebas tinimbang dalan pitakon lan wangsulan sing wis ditemtokake. Templat visual, kayata garis wektu sajarah lan peta heksagonal telung dimensi, nampilake kawruh kanthi cetha. Antarmuka responsif ndhukung tema padhang lan peteng.
The Recognition
About the A' Design Award Perak
Hive AI Platform Pemetaan Kawruh menang A' Design Award Perak, pangenalan sing prestisius banget sing diparingake marang desain tingkat dhuwur, endah lan nggumunake. Pemenang Perak iku karya sing sumunar banget lan kreatif banget sing nggambarake kaprigelan, bakat lan kaweruh para desainer. Hive AI Platform Pemetaan Kawruh dikarepake dadi karya sing nggumunake, conto teladan, profesional banget lan inovatif banget. Desain sing menang A' Design Award Perak iki iku panemuan sing luar biasa banget lan dikagumi sing paring manfaat lan nyurung maju wates seni, sains, desain lan teknologi. Hive AI Platform Pemetaan Kawruh iku ciptaan sing akeh sing ngirenake sing nggugah kekarepan, nggawa perasaan positif lan nuwuhake gumun lan kagum.
Behind the Design
Press Kit
Credits, team members and technical details provided by the designer.
- Unique Properties
- Hive AI is a transformative learning platform that enables learners to build personalized knowledge systems. With the unique “hexagon knowledge nodes” and AI-driven tools like tailored recommendations, knowledge grouping, and dynamic data visualization, Hive supports students, professionals, and researchers in fostering interdisciplinary exploration and managing complex information.
- Dimensions & Packaging
- Designed for adaptability, efficiency, and reliability, the platform features a responsive interface optimized for various screen sizes, supporting landscape and portrait modes. Users can personalize their experience with light and dark themes, while cross-device compatibility ensures accessibility. AI-driven adaptability enhances learning preferences, providing real-time interactions with secure performance. Hive AI integrates technical precision with user-friendly design, delivering a dynamic, intuitive experience tailored to individual needs.
- Tags
- Geneartive AI, Adaptive Learning, Personalization, Ed-Tech, Non-linear Interaction, Data Visualization
- Production Technology
- Hive AI's technology was developed through an iterative, data-driven approach leveraging advanced neural networks. The product mission guided development, ensuring the machine learning framework effectively organizes users' thoughts into structured knowledge. Rigorous testing and refinement perfected the node-based architecture for seamless non-linear exploration. The final system prioritizes adaptability, efficiency, and reliability, empowering users with a dynamic, personalized learning experience.
- Design Challenge
- How can node-based interactions feel as intuitive as conversation-based AI? This challenge explores motion design, iconography, and cognitive flow for seamless user adaptation. By researching existing node-based systems, the effectiveness in guiding eye movement and mental mapping was evaluated, identifying both strengths and gaps. Through user testing, motion and icons for clarity and usability were refined. The goal is to create a visually intuitive, AI-assisted system that preserves familiar interaction patterns while enhancing how users navigate complex knowledge structures with ease and engagement.
- Research
- Type:
Competitive & User-Centered Design Research
Objective:
Enhance exploratory, autonomous learning with AI-powered tools.
Methodology:
Surveys, interviews, and usability testing.
Tools:
Google Forms, SurveyMonkey, Zoom, Figma prototypes.
Participants:
100 technology enthusiasts
Results:
Optimized node connection interactions.
Improved accessibility with refined UI.
Prioritized key data visualization methods.
Insights & Impact:
AI-driven design enhances learning efficiency, accessibility, and critical thinking, fostering a more engaging and effective digital education experience. - Inspiration
- Hive AI epitomizes usability, efficiency, speed, and reliability. Drawing on generative AI and mind-mapping, it transforms learning into a dynamic, personalized journey. Market research shows 78% of users find current tools limiting, revealing a major business opportunity. With elegant simplicity, fluid design, and smart organization, Hive AI redefines learning and unleashes creativity.
- Project Duration
- Research - 1 Month
Concept and Prototyping - 2 Months
Design Finalization - 1.5 Months
Evolutives & APP Development: ongoing - Operation Flow
- The system structures fragmented information into interconnected knowledge. Learners upload and organize content - text, videos, and images - and AI enhances connections and identifies gaps.
- Node Expansion builds knowledge structures based on interests.
- Gap Bridging links concepts for deeper understanding.
Structured Organization groups information for easier memorization.
- Nonlinear Exploration enables dynamic navigation.
Hive AI also provides visual templates, like historical timelines and 3D hexagonal maps, making knowledge presentation more intuitive and effective. - Patents
- Yongwen Dai, Xuefei Wang, Keqing (Clara) Jiao, Hanyong Yang, Huiyang Chen
- Team Members
- Yongwen Dai
Xuefei Wang
Keqing (Clara) Jiao
Hanyong Yang
Huiyang Chen - Image Credits
- Yongwen Dai, Xuefei Wang, Keqing (Clara) Jiao, Hanyong Yang, Huiyang Chen
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