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Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile by Ping Chen

Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile by Ping Chen is a highly esteemed winner of the very prestigious 2026 Moputso oa Tšepe oa A' Design Award in Moralo wa Theknoloji ya Mehala, Diaplikeishene le Softwere Competition Category.


TšepeMohala· 2026

Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile

by Ping Chen

Client: Aurora

Moputso oa Tšepe oa A' Design Award Winner in Mohala Category

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Verified Award Winner

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Colors

The Design

About Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile

Mongolo wa Buka: Aurora ke sesebelisoa sa mobile se tšehetsang taolo ya maikutlo bakeng sa batho ba nang le Adhd ka tšebelisano e thehiloeng holima 'mino. Sebopeho sena se kopanya tlhahiso ea lethathamo la lipina le tsamaisanang le boemo, dengoloa ya maikutlo a mangata, le likakaretso tsa litšsupiso tsa letsatsi le letsatsi. Tngoloa e 'ngoe le e 'ngoe e hokahanya pina ea hona joale le li-slider tsa matla a maikutlo le tlhaloso e khutšoanyane. Pono ea elelo e totobatsa linako tsa bohlokoa ha 'mino o ntlafalitse tsepamiso kapa o fokotsa khatello ea maikutlo. Mokhoa oa pono o tsamaisanang le morethetho o fana ka litaelo tse bonolo tsa motsamao nakong ea ho mamela ho tšehetsa botsitso.

The Recognition

About the Moputso oa Tšepe oa A' Design Award

Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile ke mohapi oa Moputso oa Tšepe oa A' Design Award, kananelo e hlomphehang e fuoang meralo e metle e finyellang litlhoko tsa porofeshenale le tsa indasteri tse lebelletsoeng ho lintho tse hlahisitsoeng tse entsoeng hantle. Bahapi ba Tšepe ba bontša bokhoni bo boholo, bokhethehi bo phahameng, kutloisiso ea litsebi le bokhoni ba boqapi ba baqapi ba bona. Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile e lebelletsoe ho ba ntho e hlahisitsoeng e hlomphuoang haholo, e ratoang le e ratehang, e sebetsang le e nang le mehopolo e mecha. Moralo ona o hapileng Moputso oa Tšepe oa A' Design Award o kenyelletsa mekhoa e metle ka ho fetesisa ea indasteri mme o na le litšobotsi tsa theknoloji tse nang le bokhoni tse amanang le tsebo le talenta. Ka lebaka la botlalo ba eona, Aurora Mosebetsi o Moholo: Sesebelisoa sa Mobile e tšoanelehela tlhompho mme e entsoe ho fana ka ntlafatso ea bophelo e thusang ho etsa hore lefatše le be sebaka se molemo.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties
Aurora is a music-based emotional support app for people with ADHD. Unlike traditional music platforms that recommend by genre or history, Aurora learns from users' mood journals to generate state-matched playlists, highlight moments when music helped, and reveal daily regulation patterns. Rhythm-synced visuals add gentle grounding during listening. The result is a low-effort tool that turns everyday songs into long-term self-regulation habits for wellbeing support.
Dimensions & Packaging
Platform: Mobile app (iOS and Android concept). Core inputs: current track metadata, time stamp, multi-mood intensity sliders, and short text notes. Core outputs: state-matched playlists, daily mood pattern summaries, highlighted key moments, and rhythm-synced visual modes. Data model links each journal entry to a song and a time period to support calendar navigation and trend views. Visual modes include multiple selectable patterns designed for low stimulation.
Tags
ADHD support, emotional regulation, mood based playlists, music journaling, rhythm synced visuals, neurodiverse wellness, focus support, calm support, behavior design
Production Technology
Aurora is designed as a mobile app concept with interactive prototypes. The experience is structured around three modules: playlist generation, mood journaling, and visual listening mode. Mood inputs are captured with multi-dimensional sliders and saved as time-stamped entries linked to the current music track. The app then summarizes entries into daily patterns and generates state-matched playlists based on past mood and listening records. Rhythm-synced visuals are designed to respond to BPM and audio energy.
Design Challenge
The main challenge was designing for mixed emotional states without adding cognitive load. Many mood trackers force a single label, while detailed journaling can feel too demanding for ADHD users. This was addressed by using a small set of mood dimensions with quick sliders, blended visual cues to reflect mixed states, and an insight-first trend view that highlights only the most meaningful moments instead of showing every entry.
Research
Research Objectives: Understand how people with ADHD use music to manage focus and mood.
Methodology: Semi structured interviews and concept testing. Data Collection and Research Tools Used: Interview notes, affinity mapping, storyboard and UI prototypes.
Participants: 10 adults with ADHD. Results: Music helps regulation but playlists rarely match the moment and helpful tracks are hard to recall.
Insights and Impacts: Led to mood logging, pattern highlights, and state matched playlists. Effect of the Research in Real-Life Phenomena such as Business, Society and Design Itself: Supports neurodiverse wellbeing and reframes music as intentional support.
Inspiration
After interviewing 10 people with ADHD, I saw a repeated pattern: music helps them focus or calm down, but generic playlists rarely match the moment, and it is hard to remember what worked later. This gap inspired Aurora. I combined mood journaling, pattern insights, and rhythm-synced visuals to turn listening into a repeatable self-regulation tool.
Project Duration
Aurora was designed over approximately 8 weeks in the United States (Boston, MA). Work included user interviews, synthesis, concept development, wireframing, and high-fidelity UI prototyping.
Operation Flow
Users listen to music, then log a quick moment: what they are listening to, how they feel across a few mood sliders, and a short note. Aurora turns these moments into state matched playlists and a daily trend view that highlights when music helped stabilize focus or calm. A visual mode adds gentle rhythm synced motion during listening to support grounding and reduce overstimulation.
Patents
Copyrights belong to Ping Chen, 2025.
Team Members
Ping Chen
Haowei Zang

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