Research Priorities

As U.S. public schools emerged from the COVID-19 pandemic, longtime education policy wonk Liz Cohen saw that in many places, educators were finally taking tutoring seriously. 

For a year and a half in 2023 and 2024, Cohen traversed the country, interviewing educators, researchers and policymakers and observing tutoring sessions in seven states and the District of Columbia. 

Now the vice president of policy for the education group 50CAN, Cohen shares her findings in a new book, out today from Harvard Education Press: The Future of Tutoring: Lessons from 10,000 School District Tutoring Initiatives.

Upon deeper review, however, these findings leave room for optimism. First, researchers found that lower-cost virtual tutoring models — approximately $1,200/student — were just as impactful as in-person models at $2,000/student, suggesting that tutoring can be less expensive without sacrificing impact.

Second, these findings highlight what's possible when students receivetutoring that comes closer to the definition of "high-impact." For example, the effect of tutoring was largest — about 3.5 months of learning — in a New Mexico district where students received more than 2,000 minutes of tutoring per year. Across all districts in the study, this amount most closely aligned with the recommendations for implementing a high-impact model.

A new collaboration between Stanford’s SCALE and OpenAI, the creator of ChatGPT, strives to better understand how students and teachers use the popular AI platform and how it impacts learning

Education is one of the fastest-growing use cases of AI products. Students log on for writing assistance, brainstorming, image creation, and more. Teachers tap into tools like attendance trackers, get curriculum support to design learning materials, and much more.

Yet despite the rapid growth – and potential – a substantial gap remains in knowledge about the efficacy of these tools to support learning. 

A new research project from the Generative AI for Education Hub at SCALE, an initiative of the Stanford Accelerator for Learning, aims to help fill that gap by studying how ChatGPT is used in K-12 education. In particular, the research will examine how secondary level teachers and students use ChatGPT. 

Over the past couple of years, scaling well-researched solutions has been shown to also counter the negative effects of the pandemic, Loeb said, from widening achievement gaps and missed school time, to poorer social and emotional development. Her team recently launched the National Student Support Accelerator (NSSA) to address educational inequities resulting from the pandemic. NSSA conducts research on the most promising tutoring practices and works with district leaders and others to provide research-backed guidance on implementing high-impact tutoring. 

“Our students deserve this work,” Loeb said. “From our research, we learn so much about how to engage students and accelerate their learning. The practical, easy-to-use learnings from research need to reach decision makers so that our students can benefit.”

Tips by Text and NSSA are part of SCALE, Loeb’s new initiative at the Stanford Accelerator for Learning, a university-wide effort addressing some of the most challenging issues in education through research, partnerships, and technological innovation. 

Generative AI, including Language Models (LMs), holds the promise to reshape key sectors like education, healthcare, and law, which rely heavily on skilled professionals to navigate complex responsibilities. In education, for instance, effective teacher training with expert feedback is crucial yet costly, limiting opportunities to enhance educational quality on a larger scale.