
What should training change: knowledge, skill, behavior, or performance?
A practical framework for defining the workplace outcome training should produce and choosing evidence that matches it.
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LoreGraph Research
Research-backed guidance for building effective courses, improving retention, and turning knowledge into measurable progress.
Published by LoreGraph
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A practical framework for defining the workplace outcome training should produce and choosing evidence that matches it.
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A practical framework for designing learning systems around capability, practice, retention, transfer, evidence, workplace support, and responsible AI.
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The principles behind LoreGraph: capability before completion, active practice, honest evidence, human review, and learning that can survive the workplace.
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AI can generate lessons, questions, and feedback. Whether people actually learn depends on how the system makes them think, practice, and perform.
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Use a source-to-training change-control process to find affected lessons, decide who needs retraining, and preserve defensible records.
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See how July’s work strengthened LoreGraph’s document-to-training workflow, learner access, source reuse, and generation reliability.
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A practical framework for L&D teams to govern AI-created training through standards, reviews, measurement, and maintenance.
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Use a seven-level framework to match training claims with evidence—from participation and reaction to workplace application and results.
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Learn why workplace training fades and how retrieval, spacing, relevant practice, and performance support can build more durable capability.
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Learn how to design training that employees remember later and can apply when the task, timing, or workplace context changes.
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Learn why completion records participation rather than durable capability—and how to measure retention, transfer, and workplace application instead.
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Learning is more than reading documents or finishing courses. Learn the difference between information, learning, and workplace performance.
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