Orli
An interactive desk lamp that reflects AI’s behavior in response to our digital habits.
Collaborators - Sojung Pak and Joan Lee
Duration: 4 weeks
Skills: Figma, Fusion, Rhino, After Effects, Python, Laser Cutting, 3D Printing
Research Question
How might we be reflective of the usage of AI in our daily life by identifying how AI affects our digital habits?
Goals
How the system works
The interactive desk lamp, an ambient device kept on your desk, uses sensors and actuators to adapt to user interactions, showcasing AI's influence through motion and light. Progressing through customization, active, passive, and reflective phases, the system personalizes the AI experience, informing and adjusting user interactions with AI to increase or decrease usage based on preferences.
Microinteractions
The interactive desk lamp's micro-interactions span four stages: customization, active, passive, and reflective, enabling users to personalize their engagement with AI and observe its influence on their behavior. This complete cycle, from tailoring the lamp to individual needs to reflecting on digital habits, fosters a deeper understanding and mindful interaction with AI technology.
Customization
The customization phase asks users to determine their preferred level of AI interaction, adding a personal touch to the product and experience.
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Active
The active phase determines how the AI responds to the frequency and manner of user interactions.
Passive
The passive phase reflects on the user's AI interactions throughout the day, providing insights into their engagement patterns.
Reflective
The reflective phase evaluates long-term AI interaction patterns, showing how usage has influenced user behavior and engagement over time.
Tech Diagram
The interactive desk lamp uses sensors and actuators, with precise coding to dynamically respond to digital interactions. This setup reflects AI's influence on our habits through motion detection and programmable lighting, prompting user reflection.
Future Consideration
Future considerations for the project include examining more complex aspects of AI behavior to enhance its responsiveness and adaptability. We plan to gather data from a larger user base to compare usage trends on a broader scale, providing deeper insights into user interactions. Additionally, we will create detailed personas based on this usage data to better understand and cater to different user needs and preferences, ensuring a more personalized and effective experience.