Define

Task Analysis: From our secondary research, we were able to outline key tasks and the activities that users have to complete to solve the problem or achieve the goal. The task flow diagram provided us with a bird’s eye view of the problem, enabling us to focus on the design for the tasks and activities that support user goals.

Storyboard: We created a storyboard to illustrate the context in which the users completed the tasks and activities to achieve their goals. The storyboard allowed us to notice subtle details and identify areas of the user’s interaction with the product that required focus in our design

Persona: Stephanie, a college student who wishes to reduce food waste as well as increase time/money saved.

Scenario: Stephanie, a college student, discovers a fridge management app with a scanner feature that helps her reduce food waste and save money. By inputting groceries and their expiration dates, scanning items, and receiving recipe suggestions, Stephanie efficiently manages her food inventory, leading to less waste and better budgeting.

Technology: To alleviate the burden on users of manually inputting every item and expiration date into the app, we opted to integrate the Homepod & Alexa into our system. This decision was made to streamline the process and enhance user convenience by enabling voice commands for adding items and managing inventory. By leveraging these popular home systems, we provide users with a more intuitive and hands-free experience, reducing friction and encouraging consistent usage of the app to combat food waste effectively.

IDEATE

User Flow: Through several iterations, we designed the ideal steps users would follow to complete a common task with the proposed solution. As shown in the user flow diagram below, The first step the user would take to use EKO is to scan in their groceries. The user could either scan the receipt or input the groceries manually. Next, Users can ask the EKO AI for recipe recommendations based on the user’s specific wants/needs. Then user can get the most out of their groceries through customized recipes and become informed on what to do with scraps/trash. Lastly, users can rate their meals and add them to their favorites.

Privacy: In our system design, the data collected primarily includes grocery items, their quantities, and expiration dates. This data is essential for inventory management and facilitating features such as meal planning and waste tracking. Additionally, user interactions with the AI chat feature, including recipe requests and sustainability tips, may also be logged to improve the system's functionality and user experience. We only collect essential data necessary for the app's core functionalities, limiting the amount of personal information gathered from users. We obtain explicit consent from users before collecting any data, ensuring transparency and allowing users to make informed decisions about their data usage. Users have granular control over their data, including the ability to review, modify, or delete their information within the app settings.

Sketches: After finalizing our user flow, we sketched our concept and explored various aspects of user interaction by visualizing them

LO-FI PROTOTYPE & TEST

Wireframes: We iteratively developed the wireframes

Old

Evaluation: The initial version of the wireframes were reviewed by 3 peer UX designers. Each evaluator recieved a clear overview of the problem statement, proposed solution, user flow diagram, and wireframes to help understand the context

The peer UX designers provided insightful feedback on the wireframes, emphasizing the need to delve into the psychological drivers of food waste and user reluctance towards meal planning apps. They stressed the importance of simplifying the process of adding items to the app, suggesting automation or voice control integration like Alexa. Additionally, they recommended features to prompt users to remove items post-cooking and refine button options for clarity. Suggestions also included streamlining screens, ensuring consistency in headings, and renaming sections for better user understanding. Furthermore, they proposed leveraging app notifications based on inventory data to remind users of expiring items, highlighting the importance of user-centric design and convenience in addressing food waste effectively.

After carefully considering the peer reviews, we opted not to make further adjustments to the low-fidelity prototype and proceeded directly to crafting the high-fidelity version. Throughout the HI-FI design process, we ensured to incorporate the insights gleaned from the peer reviews, thereby enhancing the overall quality and user experience of the prototype.

HI-FI PROTOTYPE & TEST

Interactive Prototype: The wireframes were then further developed into an interactive prototype

Heuristic Evaluation: A new set of 3 peer UX designers conducted a heuristic evaluation of the first version of the hi-fi prototype.

The heuristic evaluation of the first version of the hi-fi prototype revealed several noteworthy findings. While the integration of the fake AI feature received positive feedback for its realism and practicality, concerns were raised regarding the visibility of certain features like the rating system. Functional issues, such as missing login screens and uncertainties about certain screen functionalities, were identified, indicating areas for improvement in-app navigation and usability. Design inconsistencies, including overlapping elements and readability issues, were noted in various sections like the tips screen and recipe titles. Recommendations were provided to enhance user control and freedom, error prevention, help and documentation features, and user feedback mechanisms. Additionally, suggestions were made to improve user interaction, such as adding confirmation pop-ups and enhancing the usability of certain features like inventory labels and recipe exploration. Overall, the evaluation highlighted the importance of addressing usability issues, ensuring consistency, and enhancing user control throughout the app design.

These findings were then incorporated into the final version of the prototype as shown in the figures below:

Landing Page

Finally, we developed a landing page to promote our system.