We tested the usability on current TikTok functionalities to find the bias issue among the users.

We have conducted 3 rounds of interview, including a Think-aloud protocol, a pilot testing interview, and a semi-structured interview with direct-storytelling method on 3 participants.

We collected interpretation notes from those interviews, analyzed them through Affinity Diagram and User Journey Map, and generated higher levels of insights. Here are a few questions that we pay most attention to:

Through interpretation session, we analyze users' need, motivation and behavior, then we grouped and labeled them in an affinity diagram, synthesizing the insights in first-person angle. Users claimed their preference, activities and findings in TikTok, shared their needs, suggestion, complaint, and concern.

We built models, user journey map, to help us better summarize and understand users' stance from the interview. This bridges the opinions from users and the following speed dating design implications.

We have also conducted a survey with 13 questions in 3 categories: app features, advertisements and purchase behaviors on Google Forms to verify our preliminary insights from qualitative research. We received 32 responses, and these results helped us iterate on our insights and following speed dating session.

We used speed dating on our insights to help us explore possible futures, validate needs and identify risk factors.
The first one is recognized by most participants.

Building on the initial speed dating concept, we conducted a contextual prototyping session using our lo-fi prototype with 7 participants. During these sessions, we identified three critical moments in their user experience: the moment they open TikTok, when they scroll consecutively, and when they watch an ad to the end multiple times. To further explore these moments, we created a physical overlay for our interactive hints and tested them in real-life environments where people typically use TikTok, directly on participants’ phones.

Here we present our final design solutions for biased ad personalization. Since our solution is represented in the form of extension, we leverage the similar component/style of current TikTok UI design and integrate our extension into the current user flow.

The dining experience is shaped not only by the food or the rituals around it, but also by the personal memories it evokes. Even an ordinary meal with family, friends, or loved ones holds deeper meaning. We wonder: What if we could capture these moments in a tangible form and share them with others?

Dining connects us to memories and sparks ideas of food, culture, and the stories that unfold around the table. Inspired by the art pieces shown, a tablecloth could serve as a canvas for documenting our meals —where we sit, what we eat, what we do, and who we share these moments with. This medium, simple to fold, pack, and gift, becomes a meaningful invitation. Our friends who receive it are invited to join us as guests to celebrate these intimate and cherished moments of our lives.

We began by narrowing down to three ideas from our precedent study.
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- On-the-go fine dining experience
- Using projection to enhance dining experience
- Designing utensils to simulate conditions that hinder eating to build empathy.
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The final idea ended being a combination of aspects from the first two ideas: design and create a tablecloth that would transform to fit different table sizes and arrangements. We wanted to bring in elements of cultural exchange that we explored in the second idea, by drawing out how we eat back home with our family.

Our first prototype was a pill-shaped table linen that would cover the entire surface of the dining table. Placements for plates are marked on the linen to dictate the position of the plates on the mat. We sew on buttons and draw fold lines, which allows the user to fold the linen to serve variety of arrangements and numbers of guests. The paper prototype aimed to achieve a similar result, but in a rectangular form.

We discovered however, that in the process of creating these foldable linens, we lost our original vision for the project: To share my own personal dining experience to other people. The demarcations of plate placements made it sterile and typical. We explored using different types of fabric to allow varied arrangements, but we ended up backtracking to our very first idea. Instead of creating a table mat that can conform to any table size, we decided to simply create 1 table mat, designed for 1 person of 1 particular dining experience.

There are areas demarcated by different colors of clothes representing the family characteristics, with names attached to each plate. We also added placements for phones and tablets, with QR code to the show or app that they would often watch/use while eating. The compass in the corner is less about exact position of the table mat in reference to the Earth’s magnetic field, but rather to indicate where the TV, the center of family dinner ritual, should be in reference to the table. There is a flexible area for shared dishes defined by cute illustration.

The final tablecloth mat has different colored fabric stitched on, representing different family members. This particular mat represents Brie’s family. The Southeast corner is reserved for her brother, who often eats away from their family and in his own room. The corner is therefore detachable and reattached via buttons. Those receiving this can also scan the QR Code on the iPad area and cloth edge to see what the gift senders see during the meal and get the digital version of the manual.

For this workshop, I chose my conversation history with Alexa as my small data set. As a graduate student living on a tight budget, I shared an old house with three roommates, which had no smart home devices installed. My primary interactions with Alexa were limited to playing music and setting alarms to wake me up in the morning. Despite living with the device for nearly a year, the data it contained was surprisingly sparse. This session gives me a good chance to reflect on my relationship with it - a companion spending endless days and nights with me.
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This workshop provided an opportunity to reflect on my relationship with Alexa, a companion that silently shared countless days and nights with me. The day before the workshop, I downloaded my interaction data from Amazon and formatted it into three columns: content, date, and time.
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During the session, I started by reviewing the most recent conversations. To my surprise, I discovered several interesting, and at times, puzzling, exchanges that had slipped past my attention. As a non-native English speaker, I noticed how miscommunications occurred between Alexa and me. Alexa, being far less sophisticated than today’s advanced language models, added another layer of complexity. These misunderstandings and the relationship we built inspired me as I worked on this zine.

I categorized the conversations into three themes:
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Type 1: Correct recognition: lucky me :)
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Type 2: Alexa's fault: Due to limited speech recognition ability, Alexa can't read ambiguous commands beyond its skill set and learning algorithm. In this case, it can't parse the command and show:
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        "Audio could not be understood."
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But what happens more frequently is that I can parse what I say but wrong.
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Type 3: My fault: Voice recording only works when I press the recording button, but my English skills can't help me figure out what to say in such a short period, so it often captures nothing, which is shown as:
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        "No text stored."

Sometimes, I would also overestimate its ability, so when I said something that it apparently can't recognize, this is what it thought:
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        "Audio was not intended for this device."

By examining the data within these categories, I naturally became curious about when these conversations occurred, how their frequency evolved over time, and how both Alexa's and my attitudes and impressions shifted after each interaction. This curiosity led me to focus on two key aspects for my data visualization:
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1. A timeline of conversations within each category.
2. A trend map illustrating patterns across these categories.
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However, diving into quantitative analysis too early felt restrictive to my creative process. Instead, I took a step back and imagined these trends in my mind, carefully reviewing the dataset and jotting down notes. Through this process, I found myself working as a detective: each conversation’s content and timestamp brought up vivid memories of what I was doing at that moment. These emerging stories felt so powerful and alive that I decided to represent this experience as an interaction within the zine itself.

With a clear purpose in mind, I began prototyping. To best represent a timeline, I decided on a long diagram, which naturally lent itself to a physical form: a multi-folded map. A set of memory cards served as keys, guiding viewers to pinpoint when the stories on the cards occurred. For the comparison between myself and Alexa, I designed a two-direction slider—an intuitive medium for examining the relationship between two entities. This slider also works as a protective cover for the delicate map. With these goals in mind, the physical form took shape smoothly and naturally.

Next, I moved on to visualizing the data using Python. To enhance readability, I focused on refining the design of the legends, selecting thoughtful color schemes, and optimizing the overall layout.

The final product is a high-fidelity prototype of the zine described above. The colors represent the two entities: me and Alexa, and also subtly reflect the passage of time throughout the day (day and night). The cover is designed as a ruler, with key attitude shifts marked along. Readers are invited to explore these shifts by locating them on the map, creating an engaging, self-guided discovery experience.

Due to time constraints, I didn’t have the opportunity to iterate further on the prototype, which is a bit disappointing. Additionally, I didn’t receive much feedback from the audience during the showcase. Reflecting on my work, I realized that trying to find a cohesive story in the chart might have been somewhat futile, as the chart itself didn’t provide any additional insights. Similarly, the trend map didn’t reveal the changes I had anticipated before visualizing the data.

It seems that my relationship with Alexa hasn’t evolved in the way I had imagined. Alexa hasn’t grown better at understanding me, nor have I become more adept at interacting with it. Moreover, the dataset from Alexa doesn’t include its own feedback, so the conversation is ultimately one-sided: focused solely on my questions. Although I recalled some amusing responses from Alexa, I couldn’t represent them due to the lack of corresponding data.

Overall, the workshop was a valuable opportunity for me to reflect on my relationship with personal artifacts and to approach them with an interactive mindset. Despite the limitations, it allowed me to think critically about these interactions in a new and engaging way.