Ping Chen ᱨᱮᱱᱟᱜ Aurora ᱢᱳᱵᱟᱭᱤᱞ ᱮᱯ ᱫᱚ ᱢᱚᱵᱟᱭᱤᱞ ᱯᱨᱚᱛᱤᱡᱚᱜᱤᱛᱟ ᱵᱷᱟᱜ ᱨᱮ 2026 A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱨᱮᱱᱟᱜ ᱟᱹᱰᱤ ᱢᱟᱱ ᱟᱠᱟᱱ ᱡᱤᱛᱠᱟᱨ ᱠᱟᱱᱟ᱾

ᱞᱚᱦᱟᱢᱚᱵᱟᱭᱤᱞ· 2026

Aurora ᱢᱳᱵᱟᱭᱤᱞ ᱮᱯ

Ping Chen ᱞᱮᱠᱟᱛᱮ

ᱜᱟᱦᱟᱠ: Aurora

ᱢᱚᱵᱟᱭᱤᱞ ᱵᱷᱟᱜ ᱨᱮ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱡᱤᱛᱠᱟᱨ

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#182515 ᱞᱟᱜᱤᱫ ᱮᱣᱟᱨᱰ ᱦᱟᱞᱚᱛ ᱡᱟᱸᱪ

A' Design Award ᱨᱮ ᱧᱮᱞ ᱢᱮ ᱯᱨᱮᱥ ᱨᱤᱞᱤᱡ ᱯᱩᱨᱥᱠᱟᱨ ᱵᱮᱡ

ᱨᱚᱝ

ᱰᱤᱡᱟᱭᱤᱱ

Aurora ᱢᱳᱵᱟᱭᱤᱞ ᱮᱯ ᱵᱟᱵᱚᱛ ᱛᱮ

ᱚᱨᱳᱨᱟ ᱫᱚ ᱢᱤᱫ ᱢᱳᱵᱟᱭᱤᱞ ᱮᱯ ᱠᱟᱱᱟ ᱡᱟᱦᱟᱸ ᱮᱰᱮᱪᱰᱤ ᱦᱚᱲ ᱠᱚᱣᱟᱜ ᱢᱚᱱᱮ ᱫᱷᱟᱨᱢ (emotional regulation) ᱨᱟᱜ ᱟᱹᱯᱱᱟᱨ ᱵᱟᱵᱚᱫ ᱛᱮ ᱜᱚᱲᱚᱭᱟ ᱾ ᱱᱚᱶᱟ ᱤᱱᱴᱟᱨᱯᱷᱮᱥ ᱫᱚ ᱫᱚᱥᱟ ᱢᱮᱱᱟᱜ ᱯᱞᱮᱭᱞᱤᱥᱴ ᱛᱮᱭᱟᱨ, ᱟᱭᱢᱟ-ᱢᱳᱰ ᱰᱟᱭᱨᱤ, ᱟᱨ ᱢᱟᱲᱟᱝ ᱢᱟᱦᱟᱸ ᱯᱮᱴᱟᱨᱱ ᱥᱟᱨᱟᱱ ᱢᱤᱫᱴᱷᱮᱱ ᱛᱮ ᱢᱮᱥᱟ ᱠᱟᱫᱟ ᱾ ᱰᱟᱭᱨᱤ ᱨᱮᱱᱟᱜ ᱢᱤᱫ ᱢᱤᱫ ᱮᱱᱴᱨᱤ ᱫᱚ ᱱᱤᱛᱚᱜᱟᱜ ᱴᱨᱮᱠ ᱥᱟᱶ ᱢᱳᱰ ᱡᱚᱨ ᱥᱞᱟᱭᱰᱟᱨ ᱟᱨ ᱢᱤᱫ ᱦᱩᱰᱤᱧ ᱱᱚᱴ ᱥᱟᱶᱛᱮ ᱡᱚᱲᱟᱣᱟ ᱾ ᱴᱨᱮᱱᱰ ᱧᱮᱞ ᱫᱚ ᱚᱱᱟ ᱢᱩᱬᱩᱫ ᱳᱠᱛᱮ ᱫᱮᱠᱷᱟᱣᱟ ᱡᱷᱚᱱ ᱨᱟᱜ ᱦᱚᱸ ᱫᱷᱭᱟᱱ ᱵᱟᱲᱷᱟᱣ ᱟᱢ ᱴᱮᱱᱥᱚᱱ ᱠᱚᱢᱮᱱ ᱠᱟᱫᱟ ᱾ ᱢᱤᱫ ᱨᱤᱫᱷᱢ ᱥᱤᱝᱠ ᱵᱷᱤᱡᱩᱣᱟᱞ ᱢᱳᱰ ᱫᱚ ᱟᱹᱱᱨᱤᱧ ᱳᱠᱛᱮ ᱟᱞᱜᱟ ᱪᱟᱞᱟᱣ ᱥᱚᱱᱠᱮᱛ ᱮᱢᱟ ᱡᱟᱦᱟᱸ ᱜᱨᱟᱩᱱᱰᱤᱝ ᱨᱮ ᱜᱚᱲᱚᱭᱟ ᱾

ᱢᱟᱱᱟᱱ

A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱵᱟᱵᱚᱛ ᱛᱮ

Aurora ᱢᱳᱵᱟᱭᱤᱞ ᱮᱯ ᱫᱚ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ Aurora ᱢᱳᱵᱟᱭᱤᱞ ᱮᱯ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾

ᱰᱤᱡᱟᱭᱤᱱ ᱯᱤᱪᱷᱟ ᱨᱮ

ᱯᱨᱮᱥ ᱠᱤᱴ

ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱧᱮᱞ ᱠᱷᱚᱱ ᱠᱨᱮᱰᱤᱴ, ᱴᱤᱢ ᱥᱫᱚᱥᱭ ᱟᱨ ᱴᱮᱠᱱᱤᱠᱮᱞ ᱵᱤᱵᱚᱨᱚᱬ ᱮᱢ ᱟᱠᱟᱱᱟ᱾

ᱟᱞᱜᱟ ᱜᱩᱱ ᱠᱚ: 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.
ᱢᱟᱯ ᱟᱨ ᱯᱮᱠᱮᱡᱤᱝ: 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.
ᱴᱮᱜ ᱠᱚ: ADHD support, emotional regulation, mood based playlists, music journaling, rhythm synced visuals, neurodiverse wellness, focus support, calm support, behavior design
ᱛᱮᱭᱟᱨ ᱛᱮᱠᱱᱚᱞᱚᱡᱤ: 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.
ᱰᱤᱡᱟᱭᱱ ᱪᱮᱞᱮᱧ: 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.
ᱜᱚᱵᱮᱥᱚᱱᱟ: 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.
ᱯᱨᱮᱨᱚᱱᱟ: 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.
ᱯᱨᱚᱡᱮᱠᱴ ᱥᱚᱢᱚᱭ: 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.
ᱠᱟᱹᱢᱤ ᱦᱚᱨ: 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.
ᱯᱮᱴᱮᱱᱴ ᱠᱚ: Copyrights belong to Ping Chen, 2025.
ᱴᱤᱢ ᱥᱮᱫᱟᱭ ᱠᱚ: Ping Chen
Haowei Zang