Finding the time to plan nutritious meals is very difficult for college students. Our team wanted to find a way to make this process easier for students. We designed Chomp, an app that helps students plan meals based on their schedules and assists with recipes through the use of a chatbot.




Nehema Kariuki
Caitie Cardwell
Huanyuan Li


Product Design


UI Designer

Hunt statement

Finding the time to plan nutritious meal is very difficult

We learned about the difficulties that college students were having surrounding meal planning and grocery shopping. We sent out surveys, conducted user interviews and researched the popularity of current meal planning apps in order to inform our design and gain a better understanding of the problem space.


Gaining insight & empathy

We used various research methods to better understand the problem space that we were solving for. We conducted a market analysis, sent out a survey to students on campus and synthesized our findings with an affinity diagramming exercise.

Secondary research

Our team conducted secondary research on the different meal planning apps that already existed. We were looking to see if there were apps out there that were targeted towards students and took the users schedules into account.

Primary research

We sent out a survey to various student organizations on campus and conducted interviews with 6 participants. All of the individuals who we collected data from were students at the University of Michigan. The participants came from a variety of majors on campus and were a variety of genders, races and ages.

Sample questions:
What is your current comfort level and ability cooking?
What meals do you plan out the most vs. the least?
How often do you have questions about a new recipe?
How do you usually solve issues while cooking?How do you currently store or organize recipes?How do you feel about exploring and experimenting with new recipes?
How do you decide which groceries to buy?
How often do you buy groceries?
How often do you throw out ingredients?
How much time are you willing to spend planning a meal?
How would notifications regarding your pantry items be helpful?

Feedback and data synthesis

We then took the data that we received from both the interviews and the survey and created an affinity diagram.
We separated our data into 5 different categories: Time, Shopping methods, Exploration and comfort levels, questions, recipe storing and what to make/waste.


Through affinity diagraming, we found that:

- Participants plan out dinner the most and breakfast the least
- The majority of participants hesitate to try new recipes because they are afraid of failure
- The majority of participants use google to solve any of their cooking related issues
- Participants throw out groceries more than they would like to
- Participants are only willing to spend about 15 minutes meal planning
- The majority of participants don't have an organized method for storing recipes

Concept generation

Research in action

With these findings in mind, we started to brainstorm different features that we could include in our design. We came up with 20 potential ideas:


From the 15 ideas generated our team selected the most relevant ones and created storyboard scenarios for each of the concepts. After creating the storyboards we brought them back to our interviewees to do a concept validation and to get a gauge of what features would be most useful.

Concept validation

After reviewing our storyboards with the interviewee's, we were able to identify which features would be helpful to include. From here we narrowed down our key features to:

- Accounting for allergies and dietary preferences when recommending recipes
- Tracking current pantry items
- recommending recipes based on current pantry items
- Being able to answer questions that the user has about recipes
- Accounting for the users schedule when recommending recipes
- Generating meal plans based on the users schedule and previously cooked recipes

Think aloud

Based on the feedback from the concept validation our team developed an initial lo fidelity mockup and a chatbot flow. We then used the Think Aloud method to test these designs with 5 participants.

Final Design

Meal planning made simple

Our final design is a meal planning app that takes into account a users schedule when making meal plans. There is a chatbot integration that makes it easier to ask questions about recipes, make grocery lists and meal plans and update allergy information.

Style guide

Video sketch

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