NIST AI Risk Management Framework: A Practical Lens for Responsible AI Use
NIST’s voluntary AI Risk Management Framework offers a repeatable way to consider AI risks: set accountability, map the context, measure relevant risks, and manage them over time.
National Institute of Standards and Technology
08 September 2026Year 2007PaperThe Power of Feedback
A feedback framework focused on where the learner is going, how they are going, and what should happen next.
John Hattie and Helen Timperley
01 March 2007Year 2013PaperImproving Students' Learning With Effective Learning Techniques
Reviews ten common learning techniques and rates practice testing and distributed practice as especially useful across conditions.
John Dunlosky, Katherine A. Rawson, Elizabeth J. Marsh, Mitchell J. Nathan, and Daniel T. Willingham
08 January 2013Year 2011PaperRetrieval Practice Produces More Learning than Elaborative Studying with Concept Mapping
Compares retrieval practice with concept mapping and shows that active reconstruction can drive meaningful learning.
Jeffrey D. Karpicke and Janell R. Blunt
11 February 2011Year 2006PaperTest-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention
Shows that retrieval through testing can improve later retention more than repeated study, especially over longer delays.
Henry L. Roediger III and Jeffrey D. Karpicke
01 March 2006Year 2005PaperCognitive Reflection and Decision Making
Introduces the Cognitive Reflection Test as a compact way to study whether people override tempting first answers.
Shane Frederick
01 November 2005Year 1974PaperJudgment under Uncertainty: Heuristics and Biases
A foundational account of availability, representativeness, and anchoring as useful shortcuts that can also create predictable judgment errors.
Amos Tversky and Daniel Kahneman
27 September 1974