• California Gave Schools More Autonomy. The Result Is a Warning for Every Executive

    | Inc.com

    California’s experiment with local control reveals what happens when organizations grant autonomy without clear standards, ownership, or support.

    A new school year begins for California students this week with fresh data for administrators. Stanford University released an 18-month study of California’s K-12 public school system on May 7, 2026, with contributions from 112 researchers.

    The project, led by Stanford professor Susanna Loeb and titled “Getting Down to Facts,” found that California’s fragmented governance structures leave no single agency clearly in charge of fixing struggling schools. An AI analysis of 7,000 district planning documents found that only 7.9 percent of local goals had a measurable target. 

    The study found administrators spend nearly 20 hours a week on compliance paperwork. Researchers also found that two-thirds of California school districts have gone through at least one superintendent transition since 2019, and school board turnover is climbing too. 

  • Chris Agnew - Moving from AI Promise to Learning Impact

    | The Brainwaves Video Anthology

  • Please Vote for Our SXSW EDU 2027 Panel Picker Proposals

    | SXSW PanelPicker

    We're excited to share our three SXSW EDU 2027 proposals with you! Community votes help determine which sessions are selected for the conference. Please click the links below to review our proposals and vote for the ones you find most compelling. Voting ends on August 23, 2026, so don't wait! Your support means everything—thank you for helping us bring these important conversations to SXSW EDU!

    A National Research Agenda for AI in Education (Vote)

    Teens, Metacognition, & ChatGPT: Early Lessons from Estonia (Vote)

    The Evidence Standard: What Actually Works in AI Ed (Vote)

  • Tomiko Brown-Nagin appointed dean of Stanford Graduate School of Education

    | Stanford Report

    Tomiko Brown-Nagin, one of the nation’s leading experts in educational law and policy, history, and constitutional law, will be the next dean of the Stanford Graduate School of Education (GSE), Provost Jenny Martinez announced today.

    Brown-Nagin joins Stanford from Harvard Radcliffe Institute, where she has served as dean since 2018. She will begin her appointment as dean of the GSE on Nov. 1, succeeding Daniel L. Schwartz, who has served as the I. James Quillen Dean for the past 11 years.

    Brown-Nagin is a distinguished interdisciplinary scholar whose research is deeply entwined with the history of education in the United States and educational law and policy.

  • The ABCs of California Education

    | Getting Down to Facts III

  • Introducing Claude for Teachers

    | Anthropic

    Decades of research show that practices like differentiation, mastery-based learning, and small group instruction reliably improve student achievement, but teachers are often short on time and resources to implement them. Budgets are stretched, classes may be too large to meet every student's individual needs, and planning often spills into evenings. This strain is heaviest in under-resourced schools. Claude for Teachers is designed to close the gap between educational best practices and what a teacher's week allows.

    Early evidence suggests that while the impact of AI tools for students is mixed and depends on the implementation, AI tools for teachers can strengthen instructional practice and improve student outcomes. This is the aim of Claude for Teachers: support the craft behind great teaching and protect what teachers value most—time with their students.

  • High-Impact Tutoring, With A Twist

    | Forbes

    High-impact tutoring (HIT), or high-dosage tutoring, is widely endorsed by educational leaders, advocates, and researchers to help improve outcomes for students. HIT is defined by several key components, all of which have been employed by Read Alliance since its inception (and long before the terminology of “high-impact tutoring” or “high-dosage tutoring” was defined and in use):

    • Frequent (3+ times per week)
    • Small group size (no more than four students per tutor, with one-to-one tutoring as the preferred model)
    • Targeted instruction focused on specific skills or learning gaps, and designed to complement classroom instruction
    • Led by trained instructors who receive ongoing support and oversight
    • Consistent tutor-student relationship that fosters a strong connection over time

    These components are largely attributed to Stanford University’s National Student Support Accelerator (NSSA), in which Read Alliance (READ) is listed as a vetted provider which means the READ model meets their standards for excellence widely recognized as the “stamp of approval” for HIT programming.

  • Q&A: Chris Agnew on AI and the Future of Schooling

    | FutureEd

    What is the AI for Education Hub at Stanford’s SCALE Initiative and how did you come to lead the organization?

    The goal of the Hub is to explore what’s working and what’s not with AI in education and to study how to use AI to reimagine longstanding education systems to better benefit kids.

    I spent two decades in education, almost entirely in non-traditional learning environments, like outdoor- and community-based classrooms and apprenticeships. I left that space feeling frustrated because I knew that immersive, experiential, relevant learning was impactful for kids, but it’s way too expensive to be accessible to all.

  • Study: Giving Kids Access to AI Tutors Doesn’t Mean They’ll Use Them

    | The 74

    Ed tech companies routinely pitch AI tutoring platforms as a way to deliver personalized instruction at a scale that no human teacher can match. But when researchers from Stanford University looked at how much students actually used one major AI platform, something startling happened: Students didn’t use it that much at all. 

    In the study, published Wednesday, two unnamed school districts carved out dedicated time for hundreds of elementary school students to work with a well-known AI reading tutor, either during class time or after school. Researchers followed about 350 students across two randomized controlled trials. All of the students were expected to log on for at least two 30-minute sessions a week.

  • Research on AI tutoring ran into a problem: Most students wouldn’t use it

    | Chalkbeat

    A group of Stanford University researchers started with one question: Could a human tutor providing motivation and support get students to spend more time working with an AI literacy tutor?

    The answer turned out be yes — but only between one and four minutes more per week. Many students never logged on at all.

    That left the researchers with a different set of questions.

    “A key finding that we weren’t even meaning to test is that having access to this AI tutor isn’t the same as using it,” said Carly Robinson, the lead author on the study released Wednesday and the director of research for the SCALE Initiative at the Stanford Accelerator for Learning.