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AI Challenges Core Assumptions in Education
| The Stanford Institute for Human-Centered AI (HAI)
Last week, Stanford Institute for Human-Centered AI and the Stanford Accelerator for Learning convened educators, researchers, technologists, policy experts, and more for the fourth annual AI+Education Summit. The day featured keynotes and panel discussions on the challenges and opportunities facing schools, teachers, and students as AI transforms the learning experience.
At the summit, several themes emerged: AI has created an assessment crisis – student projects no longer indicate a strong learning process; schools are awash with too many AI products and need better evaluations and sustainable adoption models; AI’s benefits aren’t equitable; AI literacy is a non-negotiable; human connection is irreplaceable.
Read a few of the highlights from the Feb. 11, 2026 event, and watch the full conference on YouTube.
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Research Notes: Two Emerging Strategies for Using AI in Tutoring
| FutureEd
A second study conducted by researchers at Stanford University examined a different model: Tutor CoPilot, an AI-tool designed to provide guidance to tutors during chat-based tutoring sessions. Different from LearnLM, which gives the supervising tutor only one suggested response, Tutor CoPilot gives tutors three suggested responses that tutors can choose from, edit, or regenerate. In a study conducted between March and May 2024, 1,000 elementary school students were randomly assigned to chat-based sessions with either a human tutor alone or a human tutor using Tutor CoPilot.
Students in the Tutor CoPilot condition were four percentage points more likely to achieve topic mastery than students assigned to human tutors, with the largest gains (up to 9 points) among students assigned to lower-rated and less-experienced tutors. The researchers suggest that these improvements were likely driven by the use of higher-quality instructional practices—tutors using CoPilot were 10 percentage points more likely to prompt students to explain their thinking, while tutors in the control condition were more likely to rely on generic encouragement.
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How Districts Can Fund High-Quality Tutoring Now That ESSER Money Is Gone
| The 74
High-quality tutoring has emerged as an important post-pandemic strategy for helping struggling students in public schools. Research finds that tutoring often results in substantial additional learning gains when delivered during the school day, in small groups with the same tutors and multiple times a week for at least 10 weeks.
But this often comes with a substantial price tag — depending on the model and staffing approach, costs can range from $1,200 to $2,500 per student per year. During the pandemic, many districts relied on federal Elementary and Secondary School Emergency Relief funds to launch or expand tutoring programs, but these have largely expired.
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Statewide Briefing on Getting Down to Facts III
The Stanford SCALE Initiative and Policy Analysis for California Education (PACE) invite you to join us for a virtual statewide education partner briefing on the next phase of the Getting Down to Facts III project. Your leadership and perspective are essential as we work to connect rigorous research with the realities facing California’s students, families, and schools. This virtual session brings together community partners, policymakers, researchers, and education leaders to share plans, understand overlap, and collaborate on the statewide work ahead for California schools.
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Why One-on-One Tutoring Works
A recently published study from Stanford SCALE Initiative examines why one-on-one early literacy tutoring produces substantially larger learning gains than two-on-one tutoring, drawing on detailed transcript data from a large randomized controlled trial of virtual tutoring for K–2 students. Prior work from the same intervention found that one-on-one tutoring nearly doubled impacts on literacy outcomes compared to tutoring two students at a time. This study focuses on uncovering the mechanisms behind that difference.
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Catapult Learning White Paper Demonstrates High-Impact Tutoring’s Effectiveness in Generating Measurable Academic Gains for K-12 Students
High-impact tutoring is now widely recognized as one of the most effective strategies for addressing learning gaps. Research from the National Student Support Accelerator (NSSA), the Annenberg Institute’s EdResearch for Recovery, and other national studies shows that frequent, small-group or one-on-one tutoring delivered by trained tutors using high-quality curricula consistently produces significant academic gains. -
How Tutor Co-Pilot Systems Scale Teaching Capacity Worldwide
| AI CERTs
Stanford’s National Student Support Accelerator ran the largest randomized trial to date. Researchers embedded tutor co-pilot systems within 900 tutors serving 1,800 students. Overall mastery rose four percentage points over control groups. Moreover, students paired with lower-rated tutors gained nine points. World Bank teams replicated positive effects in Nigerian secondary English classes. The AI assistant there delivered 0.31 standard deviation growth within six weeks. Consequently, analysts equated the short program to almost two years of schooling.
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Susanna Loeb is named to the 2026 RHSU Edu-Scholar Public Influence Rankings
Susanna Loeb is named to the 2026 RHSU Edu-Scholar Public Influence Rankings, ranking the 200 university-based scholars in the United States who had the biggest impact on educational practice and policy last year.
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AI and the future of human learning
| School's In - Stanford Graduate School of Education
What will it mean to teach and learn in an AI-powered world? Can we use artificial intelligence to enhance, but not replace, the best of what humans do?
Recorded live in Los Angeles at Stanford’s Open Minds event, this episode of School’s In dives into how AI is reshaping education – its promises, pitfalls, and surprises. Dan and Denise welcome Stanford faculty members Judith Ellen Fan, a cognitive scientist, and Christopher Piech, a computer scientist, to the stage for a lively discussion that ranges from motivation and creativity to assessment and cheating. Together, they explore the deeply human elements of human learning and AI design, and the ways that Stanford is shaping the conversation about how humans and machines learn together. They cover several topics, including:
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