Wayfinding is a Challenge
The Met's collection is massive, spanning 5,000 years of global culture. The physical museum is similarly huge, and its scale can be a double-edged sword. It's easy to get overwhelmed with choices, especially if you don't arrive with a plan.
Our Visitors use AI for Navigation
We've observed an increase in visitors using AI to navigate the museum. This indicates a demand for personalized, real-time wayfinding.
We wondered how we can support them with tools that offer the same flexibility, while also pulling exclusively from our data.
Logistical Wayfinding
We started by exploring logistical wayfinding, and found that AI struggled with things like mapping routes.
Because there already is long history of traditional algorithmic approaches for these types of issues, we focused on personalized wayfinding instead.
"What should I see?"
Our front-desk volunteers told us that people often don't know what the museum offers, and what they actually want to see.
Personalized Wayfinding
We wondered how we can help visitors identify areas in the museum that align with their interests.
We collaborated across departments to rapidly develop ideas, and user-test quick prototypes right in our galleries.
Prototype Overview
In this article we will look at the following two prototypes:
1. The Heatmap
2. The Compromise Finder
The Heatmap Prototype
Our first prototype turns our museum map into a custom heatmap that highlights areas particularly relevant to a visitors' interest.
The Heatmap PrototypeThe Metropolitan Museum of Art
Visitors can search for anything, 'golden jewelry' for example, and explore matching artworks in each room.
The video below shows a visitor searching for different interests. Within seconds, the heatmap overview of their interest is generated. Visitors can browse the entire museum, and explore individual artworks in each room.
The Heatmap PrototypeThe Metropolitan Museum of Art
User-testing showed that the heatmap is great for targeted exploration: Repeat visitors or researchers who have a specific interest in mind.
However, for visitors who don’t know where to start, the prototype presented a "blank canvas" problem by offering too much choice. They preferred a simple first step instead.
The Compromise Finder Prototype
Our next experiment focuses on places to begin, rather than an open-ended choice of galleries and artworks.
The Compromise Finder PrototypeThe Metropolitan Museum of Art
The prototype is made for groups.
First, each person enters their interests from a broad pre-existing list of categories.
The Compromise Finder PrototypeThe Metropolitan Museum of Art
Then, Gemini suggests a shared itinerary for the group by taking everyone's interests into account.
Below is an example: Two visitors entered their must-sees, and received three suggestions based on their shared interests. AI also found highlights from each department to give them an idea of what they might find.
The Compromise Finder PrototypeThe Metropolitan Museum of Art
Visitors told us they enjoyed the collaborative itinerary that takes everyone’s interests into account.
By selecting galleries to begin with, it also framed the museum as a series of manageable, achievable sections.
Main Learnings
Our prototypes showed that visitors want to explore museums with more agency and personalization.
AI can unlock deep customization, allowing for real-time recommendations and suggestions that work at a range of scales, from full-museum down to individual artworks.
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