Alma Adaptívna tréningová platforma by Mina Maazi

Bronzová Vzdelávanie · 2025

Alma Adaptívna tréningová platforma

by Mina Maazi

Bronzová cena A' Design Award 2025 in Vzdelávanie
CLIENT: Birmingham City University
Estimated Number of Design Views: 24,172
Verification code F1D4926B0C · Verify this award

Visit Site View at A' Design Award Press Release

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About Alma Adaptívna tréningová platforma

Alma je asistent pre riadenie vzdelávania poháňaný AI, ktorý zlepšuje firemné školenia a digitálnu adopciu. Personalizuje vzdelávanie identifikovaním individuálnych štýlov učenia a optimalizáciou školiacich harmonogramov. Na rozdiel od tradičných metód Alma integruje mentoring medzi kolegami, poznatky riadené AI a adaptívne plánovanie, čím zabezpečuje plynulý a efektívny proces začlenenia. Znižovaním nákladov na školenie a podporou angažovanosti pomáha Alma organizáciám urýchliť rozvoj zručností pri súčasnom zlepšovaní pripravenosti pracovnej síly.

About the Bronzová cena A' Design Award

Alma Adaptívna tréningová platforma je laureát Bronzovej ceny A' Design Award, vysoko prestížneho uznania udeľovaného vynikajúcim dizajnom, ktoré sa vyznačujú kreativitou, vynaliezavosťou a bystrosťou. Laureáti Bronzovej ceny sú plne zručné a nápadne kreatívne artefakty, ktoré potvrdzujú rozsiahle skúsenosti, predstavivosť a zdatnosť svojich dizajnérov. Od Alma Adaptívna tréningová platforma sa očakáva, že bude vysoko cenený, obdivovaný a milovaný predmet, pozoruhodne profesionálny a inovatívny. Tento dizajn ocenený Bronzovou cenou A' Design Award zahŕňa najlepšie postupy v umení, vede, dizajne a technológii a vyniká pozornosťou k detailom. Alma Adaptívna tréningová platforma skvele uspokojuje potreby, dotýka sa túžob, očarúva city a vzbudzuje zvedavosť.

Press Kit

Unique Properties: Alma is an AI-powered learning assistant that personalizes corporate training by identifying learning styles and optimizing scheduling. Unlike traditional platforms, it integrates peer-to-peer learning, mentorship, and smart scheduling to reduce onboarding costs. AI-driven insights help users access relevant content, connect with peers, and enhance digital adoption while ensuring a seamless, human-centered learning experience.
Dimensions & Packaging: Alma is a cloud-based AI-driven learning platform designed for adaptive training. It integrates with Google Calendar and Outlook APIs for smart scheduling. The platform was prototyped in Figma and refined through iterative user testing. It supports responsive design, ensuring accessibility across desktop and mobile devices. End-to-end encryption safeguards user data, while scalable architecture allows seamless company-wide deployment.
Tags: Adaptive Learning, AI Assistant, Digital Literacy, Peer-to-Peer Learning, Mentorship Platform, E-Learning, Smart Scheduling, User-Centered Design, Personalized Education, Cloud-Based Platform
Production Technology: Alma was developed using AI-driven learning models and machine learning algorithms to personalize training experiences. It integrates with digital calendars for adaptive scheduling and suggests peer interactions based on shared learning topics. The Design Thinking and User-Centered Design methodologies guided the UX process. Figma was used for prototyping, while continuous user testing and feedback loops refined the system for optimal usability.
Design Challenge: The biggest challenge was integrating AI-driven adaptive learning while maintaining a human-centered approach. Developing accurate learning style detection algorithms required extensive research. We conducted both qualitative and quantitative research, including user interviews, surveys, and usability testing, to ensure an effective and engaging user experience. Additionally, overcoming resistance to digital adoption was critical. Inspired by Google and Amazon, we leveraged Peer-to-Peer Learning and Adaptive Scheduling to create a validated, user-centric platform that fosters digital readiness.
Research: This research employed User-Centered Design and Design Thinking methodologies to enhance digital literacy and onboarding. Surveys and user interviews were conducted with employees struggling with traditional training. Insights from Google and Amazon case studies highlighted Peer-to-Peer Learning and Adaptive Scheduling as key solutions. Findings shaped Alma, making onboarding more efficient, cost-effective, and engaging in corporate environments.
Inspiration: Alma was inspired by the need to improve digital adoption and corporate learning. Research showed that traditional training lacks engagement and human interaction, slowing digital readiness. By integrating peer-to-peer learning, mentorship, and adaptive AI-driven scheduling, Alma creates a seamless and practical learning experience, making onboarding more effective and reducing resistance to change.
Project Duration: The project began in October 2023 in Birmingham, UK, as part of the MA Design Management program at Birmingham City University. It was completed in November 2023 and received the Best Idea Award at the People-Led Digitalisation event, organized in collaboration with the University of Bath. The project was developed through extensive research and user-centered design methodologies to enhance corporate learning experiences.
Operation Flow: Alma streamlines learning by analyzing individual learning styles and suggesting tailored courses, mentors, and training paths. It seamlessly syncs with calendars, offering AI-powered scheduling for optimal learning sessions. Users can book mentors, track progress, and receive automated reminders. Alma fosters peer-to-peer learning, recommending colleagues with similar interests, ensuring an engaging, efficient, and interactive learning experience.
Patents: Copyrights belong to Mina Maazi, 2025.
Image Credits: Image #1: Designer Mina Maazi, ALMA UI Design, 2024.
Image #2: Designer Mina Maazi, Learning Style Detection Flow, 2024.
Image #3: Designer Mina Maazi, Adaptive Scheduling Interaction, 2024.
Video Credits: Designer Mina Maazi, ALMA User Flow Prototype, 2024.
Image #4: Illustrator John Doe, Icons from Freepik, 2024.