A Decade of AI in xAPI: Building, Not Chasing
For the team at Yet Analytics, AI in the xAPI ecosystem isn’t a recent addition or a repositioning. It’s been a continuous line of inquiry, development, and application that stretches back more than a decade. We’ve been instrumenting AI systems well before the current moment made “AI-powered” a default descriptor.
What If Your Case Studies Could Talk Back?
We’re opening up a new opportunity to explore AI-powered simulations to a small group of higher ed instructors.
Participants will get 90 days of access to the Mixta authoring platform, along with a strategy session led by a learning scientist. The goal is simple: build and run a simulation in a real course.
Why xAPI Is a High-Value Data Format for AI in Learning
Most AI initiatives in learning focus on surface-level capabilities.
Few focus on data foundations.
But AI doesn’t magically create insight. It amplifies whatever data architecture you give it.
If your activity data is fragmented, mutable, and loosely defined, AI will amplify that chaos.
If your activity data is:
Structured
Deterministic
Immutable
Standardized
AI can become a durable, scalable capability.
That’s where xAPI shines.