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.
What Comes After ADL? A Practical Continuity Check for Learning Data
With ADL now closed, a lot of organizations are facing the same quiet but important question… what happens next?
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.
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.