User Experience Researcher

Transmedia Experience | Macro UX | Week 4

We designed a transmedia experience that mobilises people to the social urgent cause of the inclusion of neurodivergent people in the workplace.


Design a transmedia experience that rallies people on urgent social cause where the voices of underrepresented groups are expressed


5 weeks | 02.02 – 09.03.2023


5 people | Cyrus (Xiyuan) Han, Mansi Chottani, Mila Tawil and Sushil Suresh


Selecting a final direction

Office desk with floating objects in 90’s Memphis style. Generated by Dale-2
Office desk with floating objects in 90’s Memphis style. Generated by Dale-2
Office desk with floating objects in 90’s Memphis style. Generated by Dale-2
Office desk environment with floating objects in 90’s Memphis style. Generated by Dale-2

Once again because we were not set in the direction and message of the item we went on spirals of thinking of different ideas which essentially made us lose valuable time in execution. What was stopping us from executing is disagreements about which direction to pursue and the technical feasibility of each option and the time required for each.

The feedback we received from our partners and lecturers did not provide a specific direction but rather highlighted the potential in both main directions. We were left to decide which direction to pursue while keeping in mind what we wanted to convey based on our audience and our group’s artistic way of expressing it.

Setting up the desk for user testing. Photo credit: Sushil Suaresh
Testing our table interaction. Photo credit: Cyrus Han

In the end, we decided to conduct a test, but we only selected the option we started thinking about last week from the various ones discussed which I think was not a great approach. This choice was based on what we felt was the most feasible option within the remaining time.

Low-fidelity prototype of our desk. Photo credit: Sushil Suaresh
Detail of low-fidelity calendar prototype on our desk. Photo credit: Sushil Suaresh
Detail of prop of our low-fidelity prototype desk. Photo credit: Sushil Suaresh
Mila testing effects for users to uncover hidden information. Photo credit: Mansi Chottani

Challenge of developing a system

The challenge was that multiple components had to convey interconnected stories, and any changes made to one “channel” would inevitably affect the others, requiring corresponding adjustments. Balancing all these factors was a complex task, as the transmedia experience had to effectively convey the intended message.

Our approach was good in terms of selecting one main channel. What I would do to improve it further is to create a diagram with the channels and specify what stories each should convey and stick to the “blueprint” as much as possible.

We then worked together to assemble the pieces, each taking on tasks based on their skill set.

Blender render of an office desk on the beach. Credit: Sushil Suaresh
Blender render of an office desk in the jungle. Credit: Sushil Suaresh
Blender render of an office desk stuck on a snowy mountain. Credit: Sushil Suaresh
Blender render of an office desk on a snowy terrain. Credit: Sushil Suaresh

My part: Coding

Most feasible architecture for the technology for my skillset behind our interaction. Arduino with sensors – To-Do App on the computer – Website: Scheme

The most feasible and quickly implementable interaction between the pieces in my opinion.

Although I have limited experience with coding in Python, I used prompting techniques to guide ChatGPT in building the solution we were after. I used provided pseudo-code (describing a computer program’s logic using plain language that closely resembles actual programming code).

Using ChatGPT in conjunction with my understanding of the programming logic from the coding languages I know, JavaScript and C++, significantly reduced the production time, and I was able to learn on the go. This experience was in stark contrast to my major project during my undergraduate studies where I had to independently learn and research a lot of information, then filter and make sense of it before applying it.

Having Sushil’s extra pair of experienced eyes made the execution very smooth. He was able to check my logic and ideas and provide troubleshooting and debugging support when I got bogged down in details or when things were outside of my expertise.

Ultrasound proximity sensor connected to Arduino board under our table. Photo credit: Cyrus Han
Troubleshooting Python code interaction.


We were able to capture the interest of visiting graduates and lecturers and received positive feedback for this stage. However, during testing with them, we realized that the interaction with the table was not clear, which reduced the intended experience. To address this issue, we will be working on incorporating extra cues into the interaction.

Read the full case study for the project!

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