What does the research say about the four-day school week?

A recent Education Week article reviewed the research on 4-day school weeks (4DSW), which are slowly growing in popularity. The idea behind the 4DSW is to cut operating costs, increase teacher recruitment and retention, and decrease burnout for students and teachers, giving struggling students extra catch-up time and giving teachers more time for planning. The results? Multiple studies reported mixed effects on student achievement, but achievement appeared to stabilize as long as enough instructional hours were provided within the parameters of the four days. In terms of teacher retention rates, a 2025 study in Texas found that teacher turnover fell 2.7% in districts that moved to a 4DSW over the course of a 17-year window. Finally, the cost savings reported have been 0.4% to 2.5%. That being said, a 2023 poll found that more than half of adults support the concept of the 4DSW, nearly double the share from two decades earlier.

Paucity of high-quality causal studies supporting AI use in K-12

A paper released in the spring from Stanford’s SCALE Initiative looked at the research evidence backing AI in grades K-12. Out of 1100 papers in Stanford’s AI Hub Research Repository, only 20 were found to be high-quality studies evaluating the impact of AI on student achievement or on educator performance. Patterns emerging from these 20 papers included that AI was most effective for students during math practice, writing tasks, and programming; that AI was most effective for students when providing hints after wrong answers versus simply providing the correct answer; and that AI was most helpful to teachers in terms of helping with lesson preparation and providing insights regarding student performance.

It is important to note the gaps in the research: mainly that there are “no high-quality causal studies of student AI use conducted in U.S. K-12 classrooms,” that most studies look at short-term versus long-term effects of AI, and that there is little research on AI and student equity, wellness, and social development.

As AI tools rapidly enter classrooms, the limited causal evidence base raises important questions. While early findings suggest some promising uses, stronger research is needed to determine what works, for whom, and under what conditions in order to ensure that AI meaningfully improves student learning.

How rigorous are education meta-analyses?

By Xue Wang, School of Education, Johns Hopkins University

Meta-analyses play a critical role in synthesizing evidence to inform education policy and practice. A new meta-review by Pellegrini and colleagues examined whether education meta-analyses follow best-practice methods for systematic reviews and statistical analysis.

The researchers reviewed 247 meta-analyses examining the effects of K–12 interventions on student academic achievement, published between 2011 and 2023. They coded for systematic review procedures, meta-analysis methods, and open science practices.

Results showed mixed adherence to best practices. Most reviews addressed research questions about both average effects (95%) and heterogeneity (88%), and used multiple search strategies. However, transparency was lacking: only 4% preregistered a protocol, 6% shared their data, and 44% reported a complete PRISMA flowchart. Few reviews (9%) reported complete search strings for all databases searched.

For meta-analysis methods, only 30% of reviews with multiple effect sizes per study used model-based methods to handle effect size dependency—the current recommended approach. The most common software was Comprehensive Meta-Analysis (45%), which does not support multilevel models for dependent effect sizes. Multiple meta-regression to explore heterogeneity was used in only 26% of reviews. While 73% conducted critical appraisal of primary studies, approaches varied widely, and no clear consensus exists on how to use quality ratings in analyses.

The authors recommend that meta-analysts preregister protocols, share data and code, use appropriate methods for dependent effect sizes, and adopt scripting software (such as R) that supports modern meta-analytic techniques.

Are education meta-analyses useful for practitioners?

By Xue Wang, School of Education, Johns Hopkins University

Meta-analyses aim to provide educators with relevant evidence to guide their decisions in practice. However, a new meta-review by Pellegrini and colleagues examined whether education meta-analyses actually use strategies that make findings relevant, applicable, and accessible to practitioners.

The researchers reviewed 103 meta-analyses of school-based academic interventions published between 2021 and 2023, coding for stakeholder engagement in the review process, reporting of study characteristics, and accessibility of findings through effect size metrics and visualizations.

Results revealed limited attention to practitioner needs. Most reviews (83%) did not mention involving stakeholders in the research process. While certain study characteristics were commonly reported—such as grade level (81%) and intervention type (62%)—others important for decision-making were rarely considered. Notably, no reviews reported the cost of materials or teacher training, information essential for educators assessing whether programs fit their contexts.

Regarding accessibility, only six reviews transformed effect sizes into metrics more meaningful for practitioners (such as Cohen’s U3). Forest plots were the most common visualization (56%), despite evidence that non-researchers find them difficult to interpret. About 39% of reviews included no visualizations at all. Only half of the reviews discussed implications for practice.

The authors concluded that while meta-analyses have potential to inform educational practice, researchers need to better engage stakeholders, report characteristics relevant to implementation decisions, and present findings in accessible formats.