Alma Systema Disciplinae Adaptabile ab Mina Maazi

Opus Alma Systema Disciplinae Adaptabile a designatore Mina Maazi est victor maxime honoratus praemii praestigiosissimi 2025 Praemium A' Design Award Aeneum in Categoria Certaminis Educatio.

AesEducatio· 2025

Alma Systema Disciplinae Adaptabile

a Mina Maazi

Cliens: Birmingham City University

Victor praemii Praemium A' Design Award Aeneum in Categoria Educatio

24,831

Victor Praemii Verificatus

Verifica Statum Praemii pro #169409

Visita Situm Vide apud A' Design Award Communicatum Insigne Praemii

Colores

Designum

De Alma Systema Disciplinae Adaptabile

Alma est adiutor administrationis discendi intelligentia artificiali auctus qui institutionem corporativam et adoptionem digitalem amplificat. Discendum personalizat modos individuales cognoscens et horaria institutionis optimans. Dissimiliter a methodis traditis, Alma mentoratum inter pares, perspicacias intelligentia artificiali ductas, et dispositionem temporalem adaptabilem integrat, experientiam initiationis facilem efficacemque praestans. Sumptus institutionis reducens et participationem fovens, Alma organizationes adiuvat progressionem facultatum accelerare dum praeparationem operariorum meliorem efficit.

Agnitio

De Praemium A' Design Award Aeneum

Alma Systema Disciplinae Adaptabile victor est Praemii A' Design Award Aeneum, honoris multum aestimati formis egregiis, creandi ingenio, sollertia et acumine insignibus dati. Victores Aenei opera perita et mire creandi sunt, quae experientiam, imaginationem et solertiam auctorum suorum confirmant. Exspectatur Alma Systema Disciplinae Adaptabile rem multum aestimatam, admiratam et carissimam, notabiliter professionalem et novam fore. Haec forma Aeneo victa optimas consuetudines artis, scientiae, formandi rationis et technologiae amplectitur et in cura minutiarum excellit. Alma Systema Disciplinae Adaptabile necessitates egregie explet, cupiditates tangit, sensus capit et curiositatem allicit.

Post Designum

Fasciculus Nuntii

Merita, sodales gregis et singula technica a designatore praebita.

Proprietates Singulares: 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.
Dimensiones et Involucrum: 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.
Notulae: Adaptive Learning, AI Assistant, Digital Literacy, Peer-to-Peer Learning, Mentorship Platform, E-Learning, Smart Scheduling, User-Centered Design, Personalized Education, Cloud-Based Platform
Technologia Productionis: 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.
Provocatio Designandi: 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.
Investigatio: 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.
Inspiratio: 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.
Duratio Operis: 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.
Cursus Operationis: 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.
Patentes: Copyrights belong to Mina Maazi, 2025.
Membra Turmae: Mina Maazi
Agnitiones Imaginum: 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.