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.
Why Your Learning Data Is Not Ready for AI (and how Learning Engineering can solve this)
Every organization is talking about AI.
Some are piloting tutors. Some are experimenting with generative feedback. Some are imagining adaptive learning pathways, automated coaching, predictive analytics, and personalized workforce development at scale. But the uncomfortable truth is that most learning data is not ready for AI.
That does not mean organizations lack data. In fact, many have too much of it. They have LMS completion records, assessment scores, survey responses, course metadata, content usage reports, platform logs, HR records, credential data, simulation outputs, and dashboard exports. The problem is not the absence of data. The problem is that most of this data was never designed to work together, and certainly was not meant to be consumed by AI.
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.
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.
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