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Stanford Summer Research Fellows Showcase Education Research
Thirteen Stanford undergraduates presented their research at the final showcase for the 2026 Summer Research Fellowship for Educational Impact on Aug. 27.
The fully funded program engages students in rigorous, faculty-led empirical research addressing critical issues in education, particularly equity. Working in a collaborative cohort, fellows build foundational research skills while contributing to interdisciplinary studies with the potential to influence education policy and practice.
Presentation topics included educational technology, literacy assessment, school desegregation and closures, student enrollment, childcare access, language processing, and school board elections.
The 2026 fellows were Aaliyah Agyen, Hallie Dong, Naomi Kao, Morgan Deale, Bryan Gonzalez, Nina Carbuccia, Aydin Alsan, Crystal Peng, Thomas Raith, Miranda (Hao) Li, Jocelyn Moreno, Joseph McDonald, and Calista Woo.
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When Does AI Help Most With Tutoring? What Emerging Research Says
To help educators navigate the AI tutoring landscape, the National Student Support Accelerator and the AI Hub for Education at Stanford’s SCALE Initiative recently published a research brief examining the current research on the effectiveness of using AI in tutoring.
As schools deal with an uncertain funding environment, they might consider using AI tutoring to reduce costs, and “we wanted to make a more actionable tool for education system leaders on what the evidence says about AI tutoring,” said Chris Agnew, the director of the AI Hub for Education, which researches AI’s impact on student learning.
Part of the challenge with researching the effectiveness of AI tutoring is that the term could refer to a wide range of tutoring models, Agnew said.
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Can Lighter-Touch Tutoring Programs Benefit Kids Too?
Intensive tutoring is supposed to be one of the most effective routes to raise student achievement. But it doesn’t always work out that way.
While meta-analyses of studies on high-dosage tutoring programs find big, positive impacts on learning, it’s often challenging for districts to implement these time- and labor-intensive initiatives in practice. Recent studies of post-pandemic tutoring programs have shown smaller-than-expected gains, or even no student improvement at all.
Could a lighter touch tutoring program be easier for schools to maintain, and still move the needle on student achievement? One new study suggests yes.
The paper comes from researchers at the National Student Support Accelerator, a program of the SCALE Initiative at Stanford University that studies high-impact tutoring. They tested a tutoring intervention called Chapter One, in which part-time tutors delivered 5-10 minute lessons throughout the school day, while students were still in their regular classrooms.
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Even With Human Help, Kids Need Motivation to Use AI Tutors. The Question Is What
| The 74
Artificial intelligence-based tutoring programs promise something schools have long struggled to provide: personalized instruction for every student at a fraction of the cost of traditional 1-on-1 tutoring.
But for that promise to be fulfilled, students have to use the AI tools. New research suggests that may be much harder to achieve than schools assume.
In a paper describing the results of randomized controlled trials led by the Stanford SCALE Initiative, 355 students in grades 1 to 5 in two school districts were assigned either to use an AI literacy tutor independently or to use the same platform with support from a human tutor. In both, the goal was to improve reading achievement.
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Homework Helper or AI Tutor? The Difference Determines What Students Learn
As the catch-all term “AI tutoring” captures the public imagination, it’s time to define and measure what actually meets the bar for a “tutor.”
A new SCALE brief from Stanford offers a useful route through this definitional problem. Instead of sorting tutoring models by how sophisticated their AI appears, AI Tutoring Is Not a Monolith places them on a spectrum of relational intensity: the depth and consistency of the human connection surrounding the student.
At the high-intensity end, a human tutor leads the instruction and holds the relationship, while AI may assist with lesson preparation, data analysis, or suggestions. In the middle, the student works directly with an AI tutor while a person oversees the process and intervenes. At the lowest-intensity, the student works with an AI-only tutor without direct human oversight.
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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.
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Please Vote for Our SXSW EDU 2027 Panel Picker Proposals
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)
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Tomiko Brown-Nagin appointed dean of Stanford Graduate School of Education
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.
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