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Hive AI Билимди картага түшүрүү платформасы by Hive AI

Hive AI Билимди картага түшүрүү платформасы by Hive AI is a highly esteemed winner of the very prestigious 2025 A' Design Award Күмүш байгеси in Интерфейс, Интеракция жана Колдонуучу Тажрыйбасы Дизайны Competition Category.


КүмүшИнтерфейс· 2025

Hive AI Билимди картага түшүрүү платформасы

by Hive AI

Client: Hive AI

A' Design Award Күмүш байгеси Winner in Интерфейс Category

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The Design

About Hive AI Билимди картага түшүрүү платформасы

Hive AI — окуучулар, адистер жана изилдөөчүлөр үчүн билимди картага түшүрүү платформасы. Платформа бытыранды маалыматты байланышкан билимге түзүмдөштүрүү үчүн алты бурчтуу билим түйүндөрүн колдонот. Колдонуучулар текст, видео жана сүрөттөрдү жүктөшөт, ал эми жасалма интеллект байланыштарды сунуштайт, тиешелүү мазмунду топтойт жана боштуктарды аныктайт. Сызыктуу эмес навигация белгиленген суроо-жооп жолдорунун ордуна эркин изилдөөгө мүмкүндүк берет. Тарыхый убакыт тилкелери жана үч өлчөмдүү алты бурчтуу карталар сыяктуу визуалдык шаблондор билимди так көрсөтөт. Адаптивдүү интерфейс жарык жана караңгы темаларды колдойт.

The Recognition

About the A' Design Award Күмүш байгеси

Hive AI Билимди картага түшүрүү платформасы – A' Design Award Күмүш байгесинин жеңүүчүсү, жогорку деңгээлдеги, көркөм жана таасирдүү дизайндарга берилген өзгөчө бедели жогору таануу. Күмүш жеңүүчүлөр – өтө тамаша жана абдан чыгармачыл иштер, алар авторлорунун мыкты билимин, талантын жана сезгичтигин көрсөтөт. Hive AI Билимди картага түшүрүү платформасы сонун, үлгүлүү, өзгөчө кесипкөй жана терең жаңычыл чыгарма болот деп күтүлөт. Бул Күмүш байгени жеңген дизайн – искусство, илим, дизайн жана технологиянын чек араларын кеңейтүүгө салым кошкон көрүнүктүү ойлоп табуу. Hive AI Билимди картага түшүрүү платформасы баардыгы кызыккан чыгарма, ал каалоону ойготот, жагымдуу сезимдерди алып келет жана таң калуу менен суктанууну жаратат.

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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