Hive AI پلتفرم نقشه‌برداری دانش by Hive AI is a highly esteemed winner of the very prestigious 2025 جایزه نقره‌ای A' Design Award in طراحی رابط کاربری، تعامل و تجربه کاربری Competition Category.
نقرهرابط کاربری· 2025
Hive AI پلتفرم نقشه‌برداری دانش
by Hive AI
Client: Hive AI
جایزه نقره‌ای A' Design Award Winner in رابط کاربری Category
Verified Award Winner
Verify Award Status for #169652The Design
About Hive AI پلتفرم نقشه‌برداری دانش
هایو ای‌آی (Hive AI) یک پلتفرم نقشه‌برداری دانش برای یادگیرندگان، متخصصان و پژوهشگران است. این پلتفرم از گره‌های دانش شش‌ضلعی برای ساختاردهی اطلاعات پراکنده و تبدیل آن‌ها به دانش پیوسته استفاده می‌کند. کاربران متن، ویدیو و تصویر بارگذاری می‌کنند و هوش مصنوعی پیوندها را پیشنهاد می‌دهد، محتوای مرتبط را گروه‌بندی می‌کند و شکاف‌ها را شناسایی می‌نماید. ناوبری غیرخطی به‌جای مسیرهای ثابت پرسش و پاسخ، کاوش آزادانه را پشتیبانی می‌کند. قالب‌های بصری، مانند خطوط زمانی تاریخی و نقشه‌های شش‌ضلعی سه‌بعدی، دانش را به‌صورت واضح ارائه می‌دهند. یک رابط کاربری واکنش‌گرا از تم‌های روشن و تیره پشتیبانی می‌کند.
The Recognition
About the جایزه نقره‌ای A' Design Award
Hive AI پلتفرم نقشه‌برداری دانش برنده جایزه نقره‌ای A' Design Award است، شناختی فوق‌العاده معتبر که به طراحی‌های سطح بالای زیبا، والا و تأثیرگذار اعطا می‌شود. برندگان نقره آثاری بسیار درخشان و فوق‌العاده خلاق‌اند که تخصص، استعداد و تیزبینی برجسته طراحان خود را نشان می‌دهند. از Hive AI پلتفرم نقشه‌برداری دانش انتظار می‌رود اثری شگفت‌انگیز و نمونه باشد که به‌طور چشمگیری حرفه‌ای و عمیقاً نوآورانه است. این طراحی برنده جایزه نقره‌ای A' Design Award اختراعی بسیار قابل‌توجه و ستوده است که سود می‌رساند و مرزهای هنر، علم، طراحی و فناوری را پیش می‌برد. Hive AI پلتفرم نقشه‌برداری دانش آفرینشی است که به آن بسیار رشک برده می‌شود، خواسته‌ها را بیدار می‌کند، احساسات مثبت می‌آورد و حیرت و شگفتی برمی‌انگیزد.
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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