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
Learn anything with the /teach skill
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
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
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Discover and learn about any topic with Learn-Anything. Our free, comprehensive platform connects you to the best resources for every subject. Start learning today!
Kevin A. Bryan - Eight Rules for Teaching in AI World
How should university teaching change due to AI? A professor's job is to decide what to teach and how to present this material. The job of the course structure is to ensure students learn that content. AI presents three issues: the link between performance and student knowledge has been broken, students need to be able to use AI effectively in their future life, and AI should let us improve how much students learn. We need to modify our courses, our expectations, and our evaluations because of AI, but we can do so in a way that makes education more effective than ever.
The Ed. Dept. Wants to Steer Grant Money to AI. What That Means for Schools
The Education Department is proposing to make advancing AI in education one of its grantmaking priorities.
Math Academy Wants To Supercharge Your Learning
A review of the online learning program
Wait… whats a Community of Practice?
“Learning is not merely the acquisition of knowledge by individuals. It is a process of social participation.” A slightly bastardised…
