Learning Research & Science
Insights culled from analysis and inquiry that keep learning professionals up-to-date on how people learn, technologies, approaches, and performance improvement practices.
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The Checklist That Didn’t Exist: A Field Guide to Cutting Extraneous Load
We design for accuracy. We design for alignment. We design for engagement, or at least what we hope will feel like engagement. We almost never sit down and ask, “How much unnecessary mental effort is this design generating, and how do we reduce it?”
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When Learning Feels Like a Vulnerability
People do not only learn content. They also interpret what needing to learn may imply about them.
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Beyond Content: Designing Learning for Performance, Inclusion & Impact
Go Beyond Content in Learning Design Learning teams are being asked to do more than ever. You’re also navigating pressure to streamline processes, connect learning to business needs, support skill development, and demonstrate impact. So, what should effective learning design prioritize? Our eBook, Beyond Content: Designing Learning for Performance, Inclusion & Impact, explores three enduring […]
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In an AI-Heavy Learning World, Are You Building Connection or Just Creating Activity?
AI, automation, and learning technologies are changing how we design, deliver, and scale learning. But as the tools become more powerful, one question becomes even more important: are we creating learning experiences that feel meaningfully human, or are we simply creating more activity? Most virtual learning includes interaction. Polls, chat prompts, breakout rooms, whiteboards, and […]
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Leveraging GenAI to Support Cognitive Development
Human‑factors research shows that automation can make simple tasks easier while exacerbating difficulty in complex tasks, underscoring the need for deliberate task allocation between humans and GenAI.
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When Performance Is Not Understanding: The Disappearance of Expertise in AI-Supported Learning
AI can generate outputs. It cannot generate the judgment that emerges from experience, productive struggle, reflection, and feedback.
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Why AI Needs Vygotsky: The Case for AI-Based Intentional Friction
The absence of desirable difficulty in AI interactions effectively ignores the biology of human learning.
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Workplace Readiness: Can Higher Education Develop AI-Ready Students?
Internships, simulations, and cross-disciplinary projects can help students practice human-AI collaboration, resilience, and decision-making in environments that mirror the workplace’s ambiguity and complexity.
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The End of the Essay & the Future of Evaluation
Professional educators understand that knowledge, comprehension, application, analysis, synthesis, and evaluation are developed through different learning methods and have to be assessed through different methods as well.
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AI Can Help Us Practice Being Human Again
The promise of AI in 2026 isn’t a workplace where machines do all the talking. It’s a workplace where machines remove noise, then help people rehearse the hardest conversations before the stakes are real