Dishcovery

Exploring the culinary terrain, we sought to understand the barriers that prevent individuals from engaging with and cooking cultural foods. Our goal was to identify these challenges and transform them into opportunities for deeper cultural connections through food.

Our need-finding mission involved face-to-face dialogues with a diverse demographic in the Bay Area—ranging from tech professionals and small business owners to artists and educators. These non-student adults, engaged in various vocations, provided a rich, nuanced understanding of the day-to-day culinary practices and the cultural significance of food in their lives.

In our need-finding phase, we engaged with a diverse array of personas (anonymised) to gain a comprehensive understanding of various cooking experiences:

• Martin: In his 30s, lacking strong cultural culinary connections, and not primarily motivated by food.
• Grace: A Taiwanese immigrant and the owner of an Asian grocery store, with insights into her customers' quests for authenticity in Asian cooking and the obstacles they face.
• Jaclyn: An immigrant from Peru and head chef at Comida Peruana, bringing a professional perspective on cultural cuisine and its preparation.
• Sofia: An immigrant from Mexico and a chef at Stanford, with a personal and professional tie to her cultural culinary roots.
• Amy: A server at Stanford's Decadence, who holds a deep sentimental connection to family recipes but faces emotional barriers to recreating them.
• Jeson: A Malaysian immigrant and founder of OpenChefs, providing a startup viewpoint on delivering authentic cultural food experiences to consumers.

We created empathy maps for each participant, such as a software engineer who misses the flavors of home and a local artist who uses food as a medium to connect with her heritage. These visual tools captured sentiments and experiences, highlighting the common thread of seeking authenticity and connection in their culinary endeavors.

• Cultural Connection: Participants like Martin expressed a desire to reconnect with their heritage, seeking authentic culinary experiences as a bridge to their cultural roots.
• Learning Preferences: Users such as Sofia showed a clear preference for hands-on, interactive learning methods, suggesting that experiential tools could significantly enhance their cooking journey.
• Authenticity in Ingredients: There's a discernible trend towards valuing the authenticity of ingredients, not just in taste but in the cultural stories they tell, as highlighted by Grace.
• Accessibility and Convenience: The ease of obtaining the right ingredients and understanding their use was a notable concern, indicating a need for accessible, user-friendly resources.
• Community and Sharing: Many expressed that food is a communal experience, highlighting the potential for shared learnings and cultural exchange within a digital platform.

The "How Might We" (HMW) questions are a set of prompts designed to open up the ideation space and encourage creative thinking. They reframe problems as opportunities for design. Our HMWs focused on making ingredients more approachable and self-explanatory for users. For instance:

• "How might we create a system where ingredients can showcase their uses and cultural significance?"
• "How might we use unfamiliarity itself to make cooking more exciting?"
• "How might we make it so that unfamiliar ingredients speak for themselves?"

After synthesizing the insights from our experience prototypes, we moved into the ideation phase. This involved an intensive brainstorming session where our team members independently proposed a total of 60 solutions, which we then compiled and analyzed for common themes. These themes were critical in guiding us toward solutions that aligned with our project's goals and user needs.

A Grocery Shopping Companion with Image Recognition

• Concept: A mobile app feature that enables users to scan an ingredient in the store and receive immediate information on its origins, recipes, and usage tips.
• Benefits: Empowers users with knowledge at the point of decision, potentially influencing healthier and more culturally diverse food choices.
• Risks: The effectiveness hinges on the quality of the image recognition software and the depth of the ingredient database.

Initial Explorations

Our initial low-fi and med-fi prototypes were aimed at testing core functionalities and gauging user interactions with the foundational concepts of our solution. This phase was crucial for exploring the user experience without the commitment to high-fidelity assets, allowing us to iterate quickly based on user feedback.

Based on our image recognition solution, the higher-level functionality of our app that we envisioned was that a user would be able to:

• Scan a foreign ingredient
• Learn about its and cultural geographical context
• Find recipes using that ingredient
• Save any recipe they encounter on the app for later use
• Explore recipes by dish, ingredient, or culture using a search function

Simple task: Locate and scan a foreign ingredient.

This task is important because it uses the central functionality of the app, which we hope would draw intrigue from users. If users are excited about the scan functionality and are encouraged to use it, this would lead to them learning more about many ingredients.

Intermediate task: Learn about the foreign ingredient.

This task is important to provide a transition between our simple and medium tasks. By learning more about the ingredient, users are encouraged to experiment with it, which was also a result affirmed by our experience prototypes.

Complex task: Cook culturally authentic dishes using that ingredient.

The fulfillment of enjoying a meal using a potentially intimidating ingredient is one of the most important results we'd like to provide for our users. Once users cook using the recipes on Dishcovery, they'll have obtained a new experience and added a new recipe to their arsenal, hopefully encouraging them to repeat the process with a different ingredient.

The Design Evolution process involved iterative improvements based on heuristic evaluations and user feedback, aimed at enhancing task flow efficiency and user experience. Here is some feedback we received which led to a redesign and eventually V2.

This iteration of the design followed even more user research and heuristic evaluation. In brief, the V3 followed usability tests on the working version of V2 on Expo (built in React Native) in order to pinpoint where the user experience could be enhanced. Below are the key screens from the final design.

• Embracing Iteration: My journey with Dishcovery taught me the power of iteration. Each prototype, shaped by user feedback, was a step towards a more refined product. This iterative cycle wasn't just about improving Dishcovery; it mirrored my own growth as a designer, becoming more adept and nuanced with each cycle.
• The Human-Centered Approach: Engaging with users from diverse backgrounds, I learned to see design through the lens of empathy. This experience deepened my understanding of design as a tool to connect and serve, pushing me to think beyond aesthetics and functionality to the core human experience.
• Valuing User Voices: Feedback became the cornerstone of Dishcovery's design process. Learning to solicit, interpret, and act on user input was a humbling process that reinforced my belief in collaborative development.

"Cooked This!" Feature

A strategically designed "Cooked This!" button would serve dual functions. On the front end, it enhances user engagement by allowing them to collect badges or achievements, showcasing their culinary journey. Each completed recipe adds to their 'Culinary Passport', fostering a sense of accomplishment and encouraging further exploration.

On the back end, this feature becomes an invaluable analytics tool, providing us with direct insights into recipe completion rates. This KPI (Key Performance Indicator) not only measures the app's engagement levels but also informs the product team about the recipes' popularity and user satisfaction. It's a subtle yet powerful method to track which cuisines or dishes resonate most with our audience, allowing us to tailor our content to user preferences more accurately.

Intelligent Personalization and AI Recommendations

Integrate a smarter recommendation system that not only analyzes user preferences and past behavior but also seasonal trends and local ingredient availability to offer personalized recipe suggestions.

Customizable User Dashboards

Allow users to personalize their dashboard to prioritize the content they are most interested in, such as highlighting favorite cuisines, dietary-specific recipes, or seasonal ingredients.

Cultural Representation and Collaboration

Partner with culinary experts from diverse backgrounds to curate and verify the authenticity of recipes, ensuring that the cultural narratives are accurately represented. This could also include a feature where users can submit their own family recipes for inclusion after a review process.

Enhanced Accessibility Features

Regular accessibility audits could lead to implementing features like voice commands for hands-free cooking assistance, high-contrast mode for users with visual impairments, and simple language options for users with cognitive disabilities.