INFERable: Leveraging xAPI to power AI Inference from Learning Activity Data
At Yet Analytics, we believe high-quality, interoperable data is the foundation of modern learning systems.
xAPI provides the standard. SQL LRS provides the infrastructure. But infrastructure alone is not enough.
The next layer is intelligence.
That’s why we’re so excited about the work of INFERable, a public benefit corporation focused on democratizing AI-driven learning analytics.
Imagine connecting xAPI instrumentation with real-time AI inference. INFERable is doing it. And they are helping organizations move from capturing learning activity to optimizing learning performance.
Who Is INFERable?
INFERable is a learning engineering company delivering Learning Analytics-as-a-Service (LAaaS™) and AI-powered Skills Architecture to help organizations generate real-time inferences from learning data.
Their mission is straightforward but ambitious:
“Democratize the power of learning analytics.”
Founded by Jim Goodell, INFERable operates as a public benefit corporation, focused on materially improving systems of education, training, and human development.
Rather than building closed, monolithic adaptive systems, INFERable provides modular AI services that integrate with interoperable data ecosystems—especially those built on international standards.
That’s where xAPI becomes essential.
From Learning Data to Learning Intelligence
Many organizations today collect activity data:
LMS events
Simulation data
Assessment outcomes
Credential records
Workforce performance signals
But most stop at dashboards.
INFERable’s platform goes further. Their AI services generate precision inferences from learning activity data—identifying patterns, predicting performance, and guiding optimization.
When paired with a standards-based Learning Record Store like SQL LRS, the workflow becomes powerful:
Learning systems emit xAPI statements.
SQL LRS stores structured and immutable interoperable event data.
INFERable’s AI models consume that data.
Real-time inferences inform feedback, personalization, and performance optimization.
This is the shift from learning data infrastructure to learning intelligence architecture.
Plug-Ins and Featured Applications
INFERable’s plug-in ecosystem enables modular deployment of analytics capabilities across learning environments. Instead of building bespoke analytics pipelines from scratch, organizations can deploy AI inference modules aligned with specific use cases, including:
Skills estimation
Mastery prediction
Adaptive feedback
Performance risk detection
Learning pathway optimization
Because their services are standards-aligned, they fit naturally into ecosystems instrumented with xAPI.
For organizations already investing in high-quality event data, INFERable adds a scalable intelligence layer.
LearningFace.ai
One of INFERable’s most exciting initiatives is LearningFace.ai, described as the “Hugging Face for Learning Analytics.”
The vision is bold:
A shared repository of precision inference detectors
Researcher-contributed models
Plug-and-play analytics for practitioners
Lower barriers to implementing adaptive systems
If Hugging Face helped democratize access to large language models, LearningFace.ai aims to democratize access to validated learning inference models.
For learning engineers, this means:
No need to reinvent inference logic
Access to evidence-based analytics
Transparent, sharable, standards-aligned models
When paired with xAPI data pipelines, this creates a distributed ecosystem where analytics models and event data interoperate seamlessly.
A Global Moment: Lifelong Learning Passport at the United Nations
INFERable recently partnered with the Learning Economy Foundation (LEF) in launching the Lifelong Learning Passport (LLP) at the 80th United Nations General Assembly.
The Lifelong Learning Passport establishes a universal, portable record of skills and achievements—connecting education, work, and opportunity across a learner’s lifetime.
This is not just another credentialing initiative. It represents infrastructure for:
Portable skills records
Global interoperability
Cross-sector recognition
AI-informed guidance across lifelong pathways
Through its LAaaS™ platform and Skills Architecture, INFERable enables real-time inference over lifelong learning records—bringing advanced adaptive feedback to learners everywhere.
For Yet Analytics, this is deeply aligned with our belief in open standards, portable data, and scalable learning infrastructure.
The Future: AI That Respects Standards
As AI becomes more embedded in learning systems, the industry faces a critical choice:
Closed, proprietary systems with opaque data flows
orOpen, standards-based ecosystems where AI operates on interoperable data
Yet Analytics and INFERable are committed to the second path.
We believe the future of Learning Engineering requires:
Transparent data models
International standards (like xAPI)
Interoperable skills architectures
Modular AI services
Open ecosystems that scale
AI is powerful—but without structured, standards-based data, it cannot reliably optimize learning.
xAPI provides the semantic foundation.
SQL LRS provides the scalable infrastructure.
INFERable provides the intelligence layer.
Together, we’re helping organizations build the next generation of learning systems.
Learn More
INFERable: https://www.inferable.app
Learning Economy Foundation: https://www.learningeconomy.io
LearnCard: https://learncard.app
Yet Analytics & SQL LRS: https://www.yetanalytics.com
If you’re interested in integrating AI-powered inference into your xAPI ecosystem, contact Yet Analytics to explore how SQL LRS and INFERable can work together in your architecture.