Join Us for a Webinar: AI Ethics in Learning, From Principles to Practice
Join us on Jul 9, 2026 at 10AM Eastern Time, for a free webinar featuring Jeanine DeFalco, PhD and Shelly Blake-Plock. In addition to celebrating the publication of IEEE 2247.4-2025, the IEEE Recommended Practice for Ethically Aligned Design of Artificial Intelligence in Adaptive Instructional Systems, we’ll be talking about how to leverage ethical frameworks to increase value within organizations.
AI Ethics for Learning: Moving Beyond Principles to Practice
From industry and workforce development programs, to government and non-profit deployments, to academic rollouts across higher education, we seek to guide organizations in frameworks for the design and implementation of ethical AI that are accessible, meaningful, and actionable. The result will increase value to customers, provide a measurable cultural impact, and present a clear and defensible return on investment.
Your Learning Data Is Organizational Intelligence. Treat It That Way.
Most learning data systems were not designed to work together. In some cases, they were actively designed to not work together.
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.
SQL LRS and the Future of Learning Data: From Storage to Intelligence
We are now operating in an AI-driven landscape where raw activity data is no longer sufficient. Logging events is easy. Extracting meaning is hard.
Machine learning systems don’t need more noise—they need:
Structured signals
Verified outcomes
Aggregated performance
Deterministic logic
In other words, they need preprocessed intelligence.
SQL LRS is designed to deliver exactly that.
Two Game-Changing AI Advantages of SQL LRS
For organizations working with learning and performance data, the biggest barrier to using AI effectively is rarely the models themselves—it’s the friction between data collection, transformation, and usable features.
This is where SQL LRS creates a significant advantage.
Ten Ways SQL LRS Turns xAPI Data Into AI-Ready Intelligence
Many Learning Record Stores still treat xAPI as a storage format: collect statements, store them, display a dashboard. That was sufficient when reporting was the goal. But AI systems don’t need dashboards. They need clean signals, structured sequences, and verified outcomes.
Mixta.ai and the Rise of 4th Generation Simulation Platforms
Simulation has always been one of the most powerful tools in learning and workforce development.
But not all simulations are created equal. From static branching scenarios to fully immersive AI-driven conversations, the technology has evolved in waves.
Mixta.ai has developed what we can confidently call a 4th generation simulation platform — AI-enabled, analytics-native, and architected for transparency from the ground up.
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.
INFERable: Leveraging xAPI to power AI Inference from Learning Activity Data
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.
From Job Descriptions to Task Portfolios
A modular blueprint for early‑career resilience and organizational talent optimization in the wake of AI
Building Bridges for AI within Enterprise Data Architectures: a use case leveraging a Learning Engineering approach in the domain of synthetic training
At the recent GIFTSym11, we presented recent applied research regarding the similarities between the function of AI-enabled intelligent tutoring systems and the process of Learning Engineering.