The primary objective of this gamification project is to engage and involve participants in a collective experience that merges technology, social interaction, and creative expression. By leveraging the principles of gamification, we aim to create an immersive and interactive environment that encourages collaboration, fosters a sense of community, and facilitates the exploration of collective intentions and realities. In order to reach this high level of immersion, we have designed 4 interactive experiences, each one is gamified differently.
To achieve our objectives, we will employ various gamification strategies throughout the project.
Visitors can be interacting in a limited time to achieve all the objective and experiences.
Max users per interaction: 5
The interaction strategies include:
To be seen by the AI
Installation recognise the users participating.
At least 2 people is needed to begin the interaction and the communication with the AI.
The AI proposes
4 moods are proposed by the AI to learn from humans.
This actions creates a real time data base to understand more in details what means a group emotion for the AI.
The AI has learnt
Now the AI is capable of establish some conclusions attending to the results of the previous stage. Then she can now, identifies some collective behaviours.
All recorded in the WEB3.0
The data results feeds a 3D model that can be converted into an NFT or digital asset that users can use into the metaverse or the blockchain in general.
Artificial intelligence, humans and metaverse
Each generated star point contributes to the growth of the artificial intelligence by adding a new node to the point cloud. The point cloud represents the neural connections of a human or artificial brain, and each new node represents a unique piece of information or experience that contributes to the overall growth of the brain-like structure.
This fractal structure is represented and connected to WEB3.0.
The AI becomes smarter based on viewer participation through a process of machine learning. Machine learning algorithms analyze the patterns and connections within the point cloud to identify trends and relationships between the different nodes. This allows the AI to learn and adapt to new information based on the collective input of the human participants.
© 2023 Espronceda Institute of Art & Culture / SLS Studio.