Australian Meat Academy Hub
Conceptual UX/UI Redesign (RFP Response)
Project context
This project is a conceptual UX/UI redesign created as a response to an RFP for the Australian Meat Academy Hub. The aim was to demonstrate how the learning platform could improve clarity, engagement, and long-term learning adoption across diverse industry users.
The Problem
The existing learning experience needed to support users with very different roles, experience levels, and time availability. Content felt dense, learning paths were unclear, and engagement relied heavily on one-off visits rather than continued learning.
Users & Assumptions
As this was an RFP response, user insights are assumption-based and informed by the brief and industry context. Representative personas were used to guide decisions around navigation, content structure, and personalisation — not as validated research findings.
Design Principles
The design is guided by a small set of principles: reduce cognitive load, prioritise clarity over complexity, support progressive learning, and design for accessibility by default. These principles shaped both the experience flow and visual hierarchy.
Experience Strategy
The hub is structured around clear learning paths, persona-based content delivery, and strong visual hierarchy. Personalisation is introduced through user preferences and behaviour, while maintaining a consistent and predictable experience for all users.
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Accessibility & Usability
Accessibility considerations include readable typography, sufficient colour contrast, consistent interaction patterns, and responsive layouts. These decisions aim to support a wide range of users and environments common in industry settings.
Measuring Success
As this is a conceptual proposal, success metrics are proposed rather than measured. Focus is placed on engagement quality — such as module completion, repeat visits, progression through learning paths, and adoption of recommended content.
Next Steps (If Implemented)
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Validate assumptions through user testing
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Refine personas and recommendations using real data
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Track engagement metrics and iterate based on results
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