Year:
Fall 2024
Duration:
2 Months
Project Members:
Shreya Deep, Ruibin Zheng, Yabing Yang
Problem
Graduate students with ADHD face unique challenges in the academic space with time-blindness and task management. This can lead to late work, forgetting assignments, and overall lack of confidence in students.
Solution
TerpSesh enables students to use body-doubling through study sessions with their peers. Body doubling, in which the student will work on tasks in the presence of others, will improve focus and motivation, and is an effective way for individuals with ADHD to boost their productivity. By connecting classmates together, students will be able to find or create study sessions tailored to their specific assignments, projects, or class in general.
Read our Medium article here!
More projects (4)
Design Process
Our team was tasked to create an inclusive design for those with cognitive impairments. The definition of "cognitive impairment" is quite broad, with many different possible communities we could design for. We decided to focus on those with ADHD, as we wanted to explore how graduate students with ADHD navigated the challenges of graduate school, and how we can leverage co-design to make an user-centered app design.
User Research
Our research process started with determining the research method we wanted to use, which was user interviews. We then designed our interview guide, and recruited two participants. After conducting both interviews, we took the notes from our interviews and conducted an interpretation session to identify the major painpoints and design opportunities.
Pictured below: Interpretation Session notes from the user interviews we conducted.
Ideation and Co-design
Next, we scheduled co-designing sessions with one user, and through 2 co-design sessions, where we all engaged in the ideation of the app and the refinement of its features. From the co-design sessions, we then produced a low-fidelity wireframe.
Prototype variation 1 we made based on the interview data.
Prototype variation 2 we made based on the interview data.
