DineAR
DineAR acts as an implicit dining assistant—quietly guiding users through unfamiliar cuisines without drawing attention.
Challenge
Dining in restaurants that serve unfamiliar cuisines often leads to uncertainty and discomfort for customers. Menus with unclear descriptions or dishes with difficult-to-pronounce names can make diners hesitant or embarrassed, reducing their enjoyment of the meal.
Proposed Solution
To address this, I designed DineAR, an implicit augmented reality (AR) dining assistant aimed at bridging the gap between user expectations and a restaurant's unique culinary interpretations. It discreetly helps users navigate unfamiliar menus, confidently pronounce dish names, and understand ingredients without drawing unwanted attention.
Design Process
Discovery:
To gain a deeper understanding of how customers interact with menus in foreign restaurants and identify their specific pain points, I conducted a naturalistic observation study in a Uyghur restaurant located in London with 3 participants of diverse cultural backgrounds:P1: Different cultural background to cuisine, new to Uyghur cuisine.
P2: Different cultural background to cuisine, familiar with cuisines similar to Uyghur.
P3: Similar cultural background to cuisine, familiar with Uyghur cuisine.
Define:
Synthesized findings using empathy maps and journey maps to deeply understand user behaviors, thoughts, and emotions in foreign dining scenarios.
Develop:
Employed the Crazy 8 method to ideate diverse concepts. Evaluated these ideas through prioritization matrices, selecting concepts high in impact and innovation while considering practicality.
Deliver:
Created wireframes and mid-fidelity prototypes in Figma. Conducted rapid prototyping sessions with users utilizing XREAL Air Pro 2 AR glasses and XREAL Beam for usability testing. Incorporated user feedback iteratively into the design.
