The Learning Policy Institute conducts and communicates independent, high-quality research to improve education policy and practice.
What counts as evidence in AI & ED: Towards Science-for-Policy 3.0
Abstract Since the 1990s, there have been heated debates about how evidence should be used to guide teaching practice and education policy, and how educational research can generate robust and trustworthy evidence. This paper reviews existing debates on evidence-based education and research on the impacts of AI in education and suggests a new conceptualisation of evidence aligned with an emerging learning-oriented model of science-for-policy, which we call S4P 3.0. Existing empirical evidence on AIED suggests some positive effects, but a closer look reveals methodological and conceptual problems and leads to the conclusion that existing evidence should not be used to guide policy or practice. AI is a new type of technology that interacts with human cognition, communication, and social knowledge infrastructures, and it requires rethinking what we mean by “learning outcomes” and policy and practice-relevant evidence. A common belief that AI-supported personalisation will “revolutionise” education is historically rooted in a methodological confusion that we call the Bloomian paradox in AIED, and based on a limited view on the social functions of education.
What counts as evidence in AI & ED: Towards Science-for-Policy 3.0
Abstract Since the 1990s, there have been heated debates about how evidence should be used to guide teaching practice and education policy, and how educational research can generate robust and trustworthy evidence. This paper reviews existing debates on evidence-based education and research on the impacts of AI in education and suggests a new conceptualisation of evidence aligned with an emerging learning-oriented model of science-for-policy, which we call S4P 3.0. Existing empirical evidence on AIED suggests some positive effects, but a closer look reveals methodological and conceptual problems and leads to the conclusion that existing evidence should not be used to guide policy or practice. AI is a new type of technology that interacts with human cognition, communication, and social knowledge infrastructures, and it requires rethinking what we mean by “learning outcomes” and policy and practice-relevant evidence. A common belief that AI-supported personalisation will “revolutionise” education is historically rooted in a methodological confusion that we call the Bloomian paradox in AIED, and based on a limited view on the social functions of education.
How Learning Happens | Seminal Works in Educational Psychology and Wha
How Learning Happens introduces 28 giants of educational research and their findings on how we learn and what we need to learn effectively, efficiently, and
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Khan Academy Blog
News, updates, and stories from the nonprofit that helps learners, teachers, and districts accelerate learning. Show all Educators ... Read more
A New Way to Learn Economics
A group of economists has put together a new introductory curriculum that’s modern, comprehensive, and freely available online.
LearnVector — A new AI company from Andrew Ng
A new AI company from Andrew Ng, with a $100M investment from Coursera — building one-to-one learning that stays with you until you've mastered new skills.
LearnVector — A new AI company from Andrew Ng
A new AI company from Andrew Ng, with a $100M investment from Coursera — building one-to-one learning that stays with you until you've mastered new skills.
FORRT - Framework for Open and Reproducible Research Training
Integrating open and reproducible science into higher education
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MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training
The Hidden Role of Software in Educational Research: Policy to Practice|eBook
Educational research often discounts the uniqueness and ubiquity of software and the hidden political, economic and epistemological ways it impacts teaching and learning in K-12 settings. Drawing on theories and methodologies from English education, critical discourse analysis, multimodal...
The Program
AI-powered curriculum focused on engagement, personalized mastery learning, and life skills workshops
AI Policy - Taylor & Francis
Learn about Taylor & Francis' AI Policy, which provides guidelines on the ethical use of artificial intelligence in research, publishing, and content creation to maintain integrity and transparency.
Mastery learning
Mastery learning is an instructional strategy and educational philosophy that emphasizes the importance of students achieving a high level of competence (e.g., 90% accuracy) in prerequisite knowledge before moving on to new material. This approach involves providing students with individualized support and repeated opportunities to demonstrate mastery through assessments. If a student does not initially achieve mastery, they receive additional instruction and support until they do. Mastery learning is based on the idea that all students can learn effectively with appropriate instruction and sufficient time, and it contrasts with traditional teaching methods that often focus on covering a set amount of material within a fixed timeframe, regardless of individual student needs.
Learning How to Learn (And 20+ Studies) – Max's Two Cents
I have been interested in cognitive science and effective learning methods for years. I’ve read multiple books and articles and put many ideas to test. In this post, I aim to synthesize my notes and provide references to scientific studies.