A growing volume of research shows college promise programs increase the likelihood that students enroll in and complete college. Place-based promise programs provide a guaranteed college scholarship for students who attend a specified school district. We examine the societal benefits and costs of a place-based scholarship program in rural Arkansas, the El Dorado Promise. Our cost framework treats tuition payments as a cash transfer that shifts the cost burden of higher education rather than causing new societal costs. Findings show the program provides societal benefits equal to $4.60 for each dollar invested. A break-even analysis shows the program produces benefits to society that exceed costs within an 81% confidence interval of effects, providing relatively high likelihood of positive returns.

GenAI has rapidly entered instructional and learning settings as a teaching assistant or AI tutor. However, less is known about how pedagogical intent connects to the learning generated within these systems, especially when student-facing AI dialogues are fine-tuned through teacher orchestration in live classrooms. This study examines a classroom deployment of a "Classroom Teaching Aide" (TASD) system, which enables teachers to author both a teacher-to-AI setup prompt (instructional scaffold) and a student-facing conversation starter to launch AI-mediated classroom discussions. We analyze a multi-subject pilot conducted in Spring 2025, involving 20 participating teachers (16 of whom implemented the system), across 39 classrooms and 77 TASD settings, yielding 1,479 student-AI conversations with 878 unique students. Using platform logs, LLM coding with human validation, and post-study teacher interviews (N=10), we characterize teacher authoring choices and link them to enacted student-AI interaction outcomes. In deployment, student-AI conversations were largely aligned with instructional intent: 71% were fully on-track, and fewer than 1% were substantially off-track. However, a persistent design-enactment gap emerged for cognitive demand: 38% of conversations under-reached the teacher-targeted DOK level, approaching 50% when targeting DOK 3. The study also shows that explicit finish lines in the prompt reduced the DOK gap by 0.22 levels (p < .001), and "no direct answers" guardrails reduced AI final-answer rates by 8.5 percentage points. These findings position teacher-authored prompt layers as critical orchestration levers that translate pedagogical intent into structured student-AI dialogue, underscoring both their promise for scalable classroom integration and the need for additional supports to reliably sustain higher-order reasoning during enactment.

A recent study published in Educational Researcher by White (2023) examined superintendent gender gaps. This work required 4 years of internet searches to identify and match superintendent names with each of the roughly 13,000 school districts in the United States. Although this study provided important insights into the superintendent gender gaps, the study is unable to examine gaps for females of color or the long-term career pathways of superintendents. The lack of a national longitudinal superintendent dataset has meant researchers and policymakers have limited insights into superintendent racial and gender gaps, turnover rates, experience, and career pathways to the superintendency. Drawing on data from the Texas State Longitudinal Data System, we offer several findings to provide a glimpse of what could be accomplished with a longitudinal dataset. Policymakers, school boards, search firms, and communities will fail to understand the full range of challenges and opportunities to diversifying and strengthening the superintendent workforce until such a dataset exists and is accessible to researchers and other interested parties. © 2024 AERA.

Knowing how policy-induced salary schedule changes affect teacher recruitment and retention will significantly advance our understanding of how resources matter for K-12 student learning. This study sheds light on this issue by estimating how legislative funding changes in Washington state in 2018-2019-induced by the McCleary court-ordered reform-affected teacher salaries and labor market outcomes. By embedding a simulated instrumental variables approach in a mixed-methods design, we observed that local collective bargaining negotiations directed new state funding allocations to substantially increase certificated base salaries, particularly for senior teachers with 16 years or more of teaching experience. Variability in political power, priorities, and interests of both districts and unions led to greater heterogeneity in teacher salary schedules. Suggestive evidence shows that state average teacher turnover rate was significantly reduced in the first year of reform. The McCleary-induced salary increase particularly reduces mid-career teachers' (8-15 years of teaching experience) mobility rate and late-career teachers' (23+ years of teaching experience) leaving rate. The McCleary-induced base salary increase has mostly null effects on teacher hiring in the first 2 years of implementation.

School districts face different costs to produce the same level of educational opportunity because of differences in student population, geographical costs of living, and district size. However, in many states, the school finance system fails to take these factors into account when distributing funds to school districts. Most prior analyses of state school finance systems focus on the relationship between district funding and the percent of low-income students in that district or the percent of emergent bilinguals, who are typically classified as English language learners (ELLs).\nWe present the first longitudinal descriptive evidence of the extent to which state school finance systems compound inequities for districts serving high concentrations of both low-income students and emergent bilinguals. We assess the extent to which high-ELL high-poverty districts are underfunded relative to otherwise similar districts in the same state and how these trends have changed leading up to and following the recession-era spending cuts.\nWe find that prior to the recession, high-ELL districts received greater funding levels than otherwise similar low-ELL districts in the same state. However, recessionary spending cuts disproportionately impacted funding for emergent bilinguals. The remaining resource advantages for high-ELL districts are concentrated in low-poverty districts. These findings are consistent across measures of funding, expenditures and staffing ratios. Finally, our cross-state analyses identify wide differences in the extent to which states allocate resources equitably across districts. We find that larger student weights for ELL and FRL students may increase funding for those students, but there is a relatively weak relationship between the size of funding weights for special populations and the degree of funding equity for those students.