An Exploratory Analysis of U.S. Academically Intensive Charter Schools (AICS)

Abstract

Considerable amounts of research have discussed “No Excuses” charter schools preparing traditionally disadvantaged students for higher education. Yet, no scholarly work has identified or investigated U.S. academically intensive charter schools (AICS) that were founded to enable students to excel academically as much as their abilities and efforts allow. Here, we offer an exploratory study of AICS, defining them, describing the lived experience of an AICS principal, presenting the first national data comparing AICS campus and student characteristics to those of other charter schools, and assessing whether, nationally, AICS succeed on their own terms, with relatively high academic achievements. The data indicate that AICS resemble other charter schools in terms of measurable campus and student characteristics. Standardized, NAEP-adjusted Z-scores reveal that AICS consistently outperform other charter and district schools in literacy and mathematics across demographic groups, with differences expanding after the COVID-19 pandemic. We end with limitations and suggestions for future research.

1. Introduction

1.1. What Are Academically Intensive Charter Schools?

In this exploratory paper, we define academically intensive charter schools (AICS), which we distinguish from “No Excuses” charter schools by their lessened emphasis on behavior and markedly different clientele. “No Excuses” schools such as the Knowledge Is Power Program (KIPP) schools aim to prepare first-generation college (and sometimes first-generation high school) students for higher education (Cheng et al., 2017). In contrast, AICS campuses aim to prepare all kinds of students to excel academically as much as their talents and desires allow, in order to gain access to the most elite U.S. colleges and “Oxbridge” campuses in England. Parents choosing such schools typically expect far more knowledge acquisition than in standard U.S. college preparatory schools. Accordingly, AICS teachers must have the intelligence and breadth to be content matter experts working in more professional and less compliance-driven school cultures (Kronholz, 2014). Considerable evidence indicates that in most U.S. public schools, athletics and social events like dances are nonnegotiable, with academics being subservient; this is reflected in the educational philosophy taught in teacher and leader preparation programs (Maranto & Wai, 2020), school superintendent contracts with the elected boards of education which only rarely mention academic performance (Maranto et al., 2017), and in the allocation of time and resources within schools (Levenson, 2022; Roza, 2010). In AICS, priorities flip: AICS unapologetically prioritize academic rigor.

These mission differences might mean that few traditional public schools seek to compete with AICS because of what conventional educators perceive as their over-emphases on academic achievement at the expense of values favored by schools of education (Hirsch, 1996; Maranto & Wai, 2020; Steiner, 2023), compliance-oriented bureaucracies (Roza, 2010), school boards (Kogan, 2025), and school administrators who, statistically, are more likely to have athletic coaching than other backgrounds (Maranto et al., 2018). Conventional educators might also see AICS as reproducing privilege, as some see traditional public schools (Demerath, 2009). Despite the seemingly high demand for their services, including among middle- and upper-income parents (Whitmire, 2015), we thus suspect that AICS compete for students and teachers primarily with other charter schools and private schools, not traditional public schools. Notably, one prominent AICS organization with multiple campuses, BASIS, created a separate for-profit private network which expanded globally, though it later sold off its Chinese and private school sites, including one California campus primarily serving Chinese immigrants in Silicon Valley (Education Next Blog, 2019).

Survey data going back to the 1990s suggest that academically talented students are more likely to be bored in school, since they learn the same material more quickly than other students, both in U.S. schools (Feldhusen & Kroll, 1991), and in schools in other countries (e.g., Golle et al., 2022). Interestingly, despite decades of U.S. school reforms, including attempts to develop national standards resembling those in other countries (Loveless, 2021), U.S. policymakers have devoted little attention toward assuring that academically talented students receive appropriate levels of challenge. Even U.S. charter schools, which are autonomous public schools run independently from school districts and funded based on the numbers of parents who choose to enroll their children, seldom focus on recruiting and serving academically talented students (White & Huang, 2021), perhaps in part since the charter movement has focused more on social equity than on serving students who are expected to succeed academically (and after graduation), no matter their schooling (Wohlstetter et al., 2013). No prior scholarship addresses AICS; there is only a journalistic Education Next article about the founding and growth of the BASIS charter management organization (Kronholz, 2014) and a second in the same outlet (Whitmire, 2015) describing BASIS, Great Hearts, as well as other U.S. charter management organizations (CMOs) appealing to academics-oriented, often middle-class parents.

To fill this niche, in this paper, we offer the lived experience of one BASIS principal, followed by statistical comparisons of AICS and other charter schools, and, finally, the first empirical test of whether AICS succeed on their own terms: compared to other charter schools and traditional public schools, do AICS have higher test scores? We find that for measurable dimensions such as ethnicity and income, AICS students resemble their peers in other charter schools. With important caveats, particularly regarding the absence of value-added data, findings regarding the academic achievements of AICS are very positive across all demographic groups, particularly following the COVID-19 pandemic. We end with limitations and suggestions for future research.

1.2. The Lived Experience of an AICS Principal

Max Weber (1947) was arguably the first social scientist to systematically discuss the importance of analysts directly experiencing social life in order to better understand and describe the motivations and behaviors of actors. In the 21st century, this has often been described as seeking and valuing the “lived experience” of social actors, often, though not always, used to better understand traditionally marginalized individuals and social groups (Boylan, 2008). Here, we detail the lived experience of an AICS principal (and coauthor) to help frame the quantitative work which follows.

The education journeys of AICS families and educators are unusual. Since these schools focus on academics, homework is an institutional norm. Starting in kindergarten, students have daily homework, building up to high school expectations of several hours of homework nightly. Most students accept the idea of agency, believing that academic work builds knowledge (Rowe, 2022). A recent BASIS San Antonio Shavano valedictorian boasted that her peers spend more time with their families than most teens because they manage their study time and party less. Graduates know what matters in life: family, education, knowledge, and productive friendships.

BASIS schools are purposeful in curricular scope and sequence, with each class planned backwards to prepare students to succeed in the next, and eventually in college. This is measured ultimately by high school Advanced Placement (AP) exam scores, which, in the U.S., are important and broadly accepted measures of high academic performance. Success on AP exams, most of which have passing rates under 50%, earns college credit at most U.S. colleges and universities (Finn & Scanlan, 2019). AP results come at the end of a long process, with benchmark assessments leading up to those exams, starting in the third grade. While taking seven AP classes as an 11th-grader might seem stressful, BASIS students have been preparing since elementary school, fitting the prescriptions of curricular essentialists like Hirsch (1996) and Steiner (2023). BASIS students take upwards of ten different classes in elementary school and nine in middle school, dwindling to seven (typically all AP) classes in high school. Students take content exams three times annually that are tailored towards the AP exams, building nuanced understanding of topics. Rigor increases as students enter the sixth grade, by which time longtime BASIS students understand the formatting and pace of exams covering the taught curriculum, with content building on prior content. These exams give BASIS leaders school- and teacher-level performance data. Cumulative exams encourage long-term academic growth. Starting in middle school, benchmark exams typically account for 30% of a course’s final grade. In high school, these are the AP exams, meaning that a student might fail the rest of the course, but pass based on a score of 5 on an AP exam.

Importantly, as Ravitch (1993) argued three decades ago, when students are judged by an external standard such as standardized tests rather than grades determined by teachers, students and teachers are united to reach the same goal, in the same way that a team and coach work together to improve skills to beat the opposing team. This lessens incentives for students and their parents to lobby teachers to lower standards, as often happens when teachers determine grades. Aligning student and teacher incentives makes AICS teaching more harmonious and easier (as in many non-U.S. schools) than in most American schools. Having considerable non-U.S. experience, BASIS founders Michael and Olga Block chose to use an external standard, AP, for just this reason.

Academic sequencing enables students to take upwards of seven AP classes at once, along with activities such as sports, honor societies, chess club, student government, and science fair. We must acknowledge that BASIS families are self-selected, with, among other things, the resources to drive their children to school, a subject we will return to below. That said, most can choose from among a range of public schools, charter schools, magnet schools, and private schools, and instead choose BASIS, drawn by high standards and expectations. Though we lack official statistics, many are South Asian or East Asian immigrants. Likewise, many teachers are immigrants, as at other high-performing charter schools which attract teachers who seek the academic cultures of their countries of origin (Maranto et al., 2014). Many BASIS teachers, particularly STEM teachers, also have substantial experience in private industry; thus, they support high achievement for all rather than sorting students. This culture is reinforced by incentives: teachers receive bonuses for each student scoring a 4 or 5 (passing) on an AP exam, a merit pay system which is not 0-sum. If all teachers excel, all receive bonuses. Enabling academic press manages pedagogical and behavioral expectations. Leaders view teachers as experts; thus, they are not told how to teach, but receive a topic list of concepts to be taught. For the non-AP courses, teachers are advised by the internal curriculum company to benchmark exam content, but are not given exam items.

BASIS students have internal motivation; whether this is grade-based or knowledge-based (or a mix of both) ultimately matters little since students push each other. For example, during a recent classroom observation, a teacher directed students to review the previous test with peers. No other direction was given, nor was there a rigid protocol or procedure. Rather than chaos, as might occur in many schools, for 50 min, students asked each other and the teacher questions, referencing their notes and textbook, until they understood their mistakes and helped peers understand theirs, with minimal time away from the task. Students did not check their phones or listen to music. Instead, in dyads and teams, the class engaged in learning from mistakes to perform better next time; they also improved their grades because the class assignment was to turn in test corrections.

BASIS provides little professional development, nor does it use teachers’ time for much beyond teaching. Brief weekly staff meetings transmit information, but teachers are treated as professionals with the latitude to teach as they see fit. Teachers are therefore able to teach as they think students will best understand the content. Student outcomes are the ultimate quality control: if a teacher cannot help students reach specified academic levels, they are let go. Low-performing teachers receive guidance but must show rapid improvement. Generally, these same high expectations exist for students. While student ownership and engagement are pillars, at strategic benchmarks, parents receive feedback to provide support. Students with failing grades receive academic support. If a student fails a test (which typically happens just three times per trimester for core subjects starting in the third grade), teachers notify parents.

Behavior management is minimal, easing the work of teachers and students. Families choose an AICS for academic rigor, and thus understand that neither teachers nor students can tolerate behavior disrupting learning. Unlike “No Excuses” charter schools (Cheng et al., 2017), BASIS and likely other AICS need not impose uniformity to keep order; a student might sport alien ears, pink hair, flip flops, and a tutu without attracting notice.

2. Materials and Methods

Identifying AICS

To operationally identify AICS, we used key terms suggested by prior charter researchers, though not previously applied to AICS as such (DeAngelis, 2023; Marshall et al., 2022; Peterson & Shakeel, 2024). School model data were collected from all charter schools in the nation for the school year (SY) 2018–2019 and again for the SY 2021–2022, for a total of 7570 and 8150 schools, respectively. This was achieved by collecting all available and relevant textual information from websites, charter applications, social media pages, and other advertising, and synthesizing the key terms into categories called foci using a combination of machine learning techniques and manual coding (White & Huang, 2021). The goal was to identify how schools self-identified regarding curricula, teaching pedagogies, and the student populations served. Using this vast array of qualitative data, we identified 189 schools that emphasized their academic rigor and thus distinguished themselves as AICS. Though many schools claim to provide an academically rigorous environment, most manage this within the context of another existing model such as STEM, “classical”, or “No Excuses.” Our operational definition of an AICS is a charter school whose sole distinguishing feature is an emphasis on academic rigor without a curricular specialization or emphasis on behavior.

We disaggregated the behavioral component to distinguish these schools from “No Excuses” schools, which similarly focus on high academic standards but do so while emphasizing behavioral discipline.

Table 1. Terms defining No Excuses and AICS.

‘No Excuses’
Both academic and behavioral standards and expectations are high
High academic and behavioral expectations
High behavioral and academic expectations
High standards in their academic work and their behavior
High standards of their academic performance and behavior
Highest academic and behavioral expectations
No Excuses
Rigorous core academic program with high behavioral expectation
AICS
Academic Competition
Academically Rigorous
Advanced Academic Achievement
Challenging academic settings
Challenging expectations
Challenging program
High academic standards
High Expectations
Rigorous curriculum
Rigorous educational environment
Rigorous educational program
Rigorous educational setting
Defining Terms by Category

shows key terms used to code the 370 “No Excuses” schools and 189 AICS. The table makes clear that the primary difference between these models is the emphasis on discipline and behavior, a hallmark of “No Excuses” schools.

3. Comparing AICS to Other Charter Schools

Financial Data, School Characteristics, and Student Demographics

Using the 2020–2021 Nerd$ dataset on school expenditures, we see that, nationally, AICS spend an average of USD 12,350 per student: 86% of this total comes from state and local sources, and 14% comes from federal sources. (The schools do not report philanthropic funds.) This resembles the national charter average, about USD 12,600, which is well below the estimated district school expenditures of USD 17,301 (Cornman et al., 2024), with exact figures varying by state funding formula and student characteristics. We used additional administrative data to better understand AICS demographic data, grade range, startup verses conversion flags, virtual school flags, and urbanicity. The data came from The National Alliance for Public Charter Schools [NAPCS] (2024), which maintains the largest national repository on charter school data, and grants access to researchers, using data from 2021 to 2022.

Table 2. School locale.

Locale	AICS	Non-AICS Charters
Rural	7%	12%
Suburban	24%	25%
Town	6%	6%
Urban	63%	57%

,

Table 3. Launch status.

Charter Launch Status	AICS	Non-AICS Charter
Conversion	6%	8%
Startup	94%	88%

,

Table 4. School age.

Years in Operation	AICS	Non-AICS Charters
20+ Years	20%	15%
15+ Years	23%	17%
10+ Years	20%	20%
5+ Years	31%	24%
First 5 Years	6%	23%

,

Table 5. Demographics.

	AICS	Non-AICS Charter
Asian or Pacific Islander Students	5%	0.04
Black or African American Students	26%	0.24
Hispanic Students	37%	0.36
White Students	26%	0.29
Two or More Races Students	4%	0.05
American Indian / Alaska Native Students	0%	0.01
Native Hawaiian or Other Pacific Islander Students	1%	0
Free and Reduced Priced Lunch Students	57%	0.59
Total Students	100,758	3,578,997

,

Table 6. Virtual status.

	AICS	Non-AICS Charters
Not Virtual	99%	90%
Virtual	1%	10%

and

Table 7. Grades served.

	AICS	Non-AICS Charter Schools
Elementary	3%	3%
Elementary|Middle	49%	40%
Elementary|Middle|High	19%	27%
Middle	8%	6%
Middle|High	8%	10%
High	12%	14%

show comparisons. AICS and non-AICS charter schools are generally similar, though AICS are somewhat older, with 43% being 15 years old or older compared to 32% for other charter schools. This may indicate higher parental demand, resulting in lower closure rates. AICS are also slightly less likely to be rural than other charter schools (7–12%), more likely to be urban (63–57%), and less likely to operate virtually, with only 1% of campuses compared to 10% for other charter schools operating online. Grade configurations are generally similar, though AICS are somewhat less likely than other charters to serve all k-12 (elementary, middle, high school) students (19% to 27%). There are no notable differences in terms of whether AICS were converted from existing schools or are new startup schools.

Regarding student characteristics, as shown in Table 5, there are no notable differences between AICS and other charter schools. Demographic and poverty (free and reduced lunch status, or FRL) percentages fall within 2% of each other for every category, save for the percentage of white students, which is slightly lower (26%) for AICS than other charter schools (29%). Generally, a lower percentage of white students is not considered an indicator of privilege (Melnick, 2023).

4. Do AICS Excel Academically?

4.1. Methods

Though far from comprehensive, literacy and numeracy rates remain the most readily available outcome data. Under the Every Student Succeeds Act (ESSA), standardized testing is required in grades three through eight, and once again in high school. The U.S. Department of Education’s EDFacts initiative provides the most comprehensive dataset on school performance nationwide. EDFacts primarily covers public schools which are eligible for federal funding and are participating in federally mandated assessments. To protect student privacy and comply with federal laws such as the Family Educational Rights and Privacy Act (FERPA), EDFacts suppresses the data from small subgroups by reporting proficiency in ranges rather than specific numbers. To address suppressed data, we calculate the midpoint of each range to estimate proficiency scores. By multiplying these midpoints by the number of participants in each group and summing these values across groups, weighted averages can be calculated. This ensures that groups with more participants contribute proportionally to the overall proficiency score.

Because each state employs distinct standardized assessments, with differing content, scoring systems, and proficiency thresholds, direct comparisons of raw performance metrics across states are inherently problematic. To facilitate cross-state analysis, we first standardized each school’s average proficiency score into a Z-score, calculated relative to the mean and standard deviation of all schools within the same state. A Z-score represents a school’s relative position in its state distribution; for example, a value of +1 indicates a performance that is one standard deviation above the state mean. This within-state normalization enables researchers to evaluate schools on a common relative scale, abstracting from the idiosyncrasies of individual state assessments.

However, while Z-scores enable within-state comparisons, they do not fully resolve cross-state comparability. Specifically, the same Z-score in two different states does not guarantee equivalent absolute levels of student proficiency, as the state mean itself may vary somewhat. Specifically, a school performing at one standard deviation above the mean in a low-performing state may not reach the same absolute proficiency level as a school at the same relative position in a high-performing state.

To address this, we incorporate data from the National Assessment of Educational Progress (NAEP), a nationally representative, standardized assessment administered uniformly across states. The NAEP provides state-level benchmarks for core subjects such as reading and mathematics, enabling us to estimate each state’s average proficiency level relative to the national average. We convert state-level NAEP scores into standardized effect sizes (i.e., mean differences expressed in national standard deviation units), which are then used as additive adjustments to the school-level Z-scores. This “NAEP-adjusted Z-score” approach situates each school’s performance not only relative to its state peers, but also accounts for systematic differences in state-level academic performance.

We acknowledge that this method assumes that the distributional properties (e.g., variance, shape) of state tests are sufficiently aligned with the NAEP to support linear translation via mean-shifting. Furthermore, this approach does not account for potential within-state heterogeneity, whereby the factors contributing to state-level NAEP performance may not be uniformly distributed across all schools. Nonetheless, in the absence of student-level crosswalks or harmonized state assessments, NAEP-based adjustment offers a pragmatic and empirically grounded means of enhancing cross-state comparability. It mitigates a key source of bias in national analyses of school performance while maintaining interpretability and statistical rigor.

By integrating Z-scores adjusted for state effects with weighted margins of error, researchers can generate robust comparisons of school performance. Weighted margins of error account for variability in subgroup sizes and ensure that aggregated scores are statistically reliable. For instance, in this paper, we exclude scores with margins of error exceeding 10 points to maintain analytical integrity. In total, this represents about 2% of students. This methodology leverages EDFacts data to create standardized, comparable measures of school performance across states and sectors, and provides valuable context for understanding the role of AICS nationally (Huang & White, 2023).

To better understand the effects of the COVID-19 pandemic and to better evaluate AICS in general, we examine performance data from pre-pandemic (2018–2019) and post-pandemic (2021–2022) periods. Our goal is to evaluate whether suppression-related uncertainty meaningfully affects the observed differences in proficiency between school types. In other words, we are not conducting traditional hypothesis tests on group means, but rather testing whether data suppression introduces enough uncertainty to undermine the observed descriptive differences. This distinction is important: we are working with population-level data—not a sample—so formal statistical inference is not required to estimate central tendencies. Instead, we use standard errors to quantify uncertainty from data suppression and assess whether the differences between group means are significant for this uncertainty. To do this, we calculate a group-level standard error (SE) based on the margins of error (MoEs) provided by EDFacts for schools with suppressed data. Specifically, for each school with a range-based estimate, we define participation (P) as the number of students tested, and compute a scaled uncertainty term MoE × P to represent that school’s potential contribution to group-level error. The group-level SE is then calculated as follows:

$$S{E}_{\left\{group\right\}}=\sqrt{{\sum }_i{\left(MO{E}_i \times P_i\right)}^2} /{\sum }_i{P}_i$$

This formula estimates the standard error of the group mean under the assumption that uncertainty is concentrated in schools with suppressed data. For schools that report exact proficiency values (i.e., no suppression), we assume zero measurement uncertainty in their contribution to the group score. While this assumption likely underestimates the true variability for non-suppressed schools, it allows us to focus specifically on whether suppression-related uncertainty is large enough to explain away the observed group differences. This directly answers the following core question: are descriptive differences between school types an artifact of data suppression?

We acknowledge that an alternative approach would be to conduct a standard difference-in-means test assuming a common underlying variance structure for each group. However, that method requires either (a) access to raw student-level data to compute within-group variances, or (b) strong distributional assumptions (e.g., homoskedasticity) that are difficult to verify given the public reporting format of EDFacts. In contrast, our approach provides a conservative, transparent method for bounding the impact of missingness in the data.

To interpret significance, we compare observed differences in average proficiency between school types to the group-level standard error. We use critical values of 1.645, 1.96, and 2.576 to represent 90%, 95%, and 99% confidence thresholds, respectively.

Finally, we emphasize that all findings are descriptive rather than causal. We do not attempt to explain why differences exist between school types, only whether they are likely to be robust to the uncertainty introduced by suppression in the data. Factors such as student demographics, school funding, or instructional design may underlie observed differences, but are not tested in this analysis. For full tables of standard errors and school-type comparisons, please refer to Appendix A.

4.2. Results: AICS Excel Academically Across Time Periods and Demographic Groups

As

Table 8. Pre-COVID ELA comparisons.

2018–19 Average NAEP Weighted Z-Score—ELA
Population	AICS	Non-AICS Charters	District
Total	0.35	−0.10 ***	0.00 ***
English Language Learners	0.65	0.25 ***	0.00 ***
Economically Disadvantaged	0.53	0.10 ***	0.00 ***
Students with Disabilities	0.28	0.05 **	0.00 ***
Asian	0.04	0.04	0.00
Black	0.36	0.21	0.00 **
Hispanic	0.96	0.27 ***	0.00 ***
White	0.51	−0.12 ***	0.00 ***
4th	0.17	0.19	0.00 **
8th	0.20	0.21	0.00 ***
HS	0.86	−0.12 *	0.00 *

For all tables, stars represent significance between AICS and other groups: * 90% confidence ** 95% confidence *** 99.7% confidence.

shows, pre-COVID-19 proficiency data using NAEP-weighted Z-scores, with district schools set as the baseline, show that from 2018 to 19, AICS outperformed the district baseline across all populations in English Language Arts (ELAs), though the differences were not significant for Asian students and barely (p = 0.05) significant for Black students. They also outperformed other charter school models in most categories, though not in all grades. As

Table 9. Post-COVID ELA comparisons.

2021–22 Average NAEP Weighted Z-Score—ELA
Population	AICS	Non-AICS Charters	District
Total	0.23	−0.10 ***	0.00 ***
English Language Learners	0.46	0.27 ***	0.00 ***
Economically Disadvantaged	0.35	0.11 ***	0.00 ***
Students with Disabilities	0.25	0.08 **	0.00 ***
Asian	0.16	0.01 ***	0.00 ***
Black	0.22	0.19	0.00 ***
Hispanic	0.84	0.30 ***	0.00 ***
White	0.46	−0.05 ***	0.00 ***
4th	0.46	−0.05 ***	0.00 ***
8th	0.37	0.04 ***	0.00 ***
HS	0.35	−0.15 ***	0.00 ***

For all tables, stars represent significance between AICS and other groups: ** 95% confidence *** 99.7% confidence.

shows, three years later, following the COVID-19 pandemic, we see AICS outperforming both district schools and other charter school models in ELA across all populations, generally with higher levels of significance. Again, it is important to note that these are not causal claims, but descriptive observations based on standardized test data.

As

Table 10. Pre-COVID Math comparisons.

2018–19 Average NAEP Weighted Z-Score—Math
Population	AICS	Non-AICS Charters	District
Total	0.32	−0.23 ***	0.00 ***
English Language Learners	0.51	0.15 ***	0.00 ***
Economically Disadvantaged	0.51	−0.05 ***	0.00 ***
Students with Disabilities	0.36	−0.08 ***	0.00 ***
Asian	0.09	−0.05	0.00
Black	0.47	0.14 ***	0.00 ***
Hispanic	0.97	0.14 ***	0.00 ***
White	0.41	−0.34 ***	0.00 ***
4th	0.22	0.12	0.00 ***
8th	0.18	0.05	0.00
HS	0.70	−0.30 ***	0.00 ***

For all tables, stars represent significance between AICS and other groups: *** 99.7% confidence.

and

Table 11. Post-COVID Math comparisons.

2021–22 Average NAEP Weighted Z-Score—Math
Population	AICS	Non-AICS Charters	District
Total	0.19	−0.25 ***	0.00 ***
English Language Learners	0.32	0.09 ***	0.00 ***
Economically Disadvantaged	0.29	−0.08 ***	0.00 ***
Students with Disabilities	0.24	−0.09 ***	0.00 ***
Asian	0.16	−0.08 ***	0.00 ***
Black	0.25	0.05 ***	0.00 ***
Hispanic	0.69	0.11 ***	0.00 ***
White	0.50	−0.26 ***	0.00 ***
4th	0.12	−0.26 ***	0.00 ***
8th	0.26	−0.17 ***	0.00 ***
HS	0.09	−0.36 ***	0.00 **

For all tables, stars represent significance between AICS and other groups: *** 99.7% confidence.

show, for mathematics, we see a similar trend. As Table 10 shows, before the COVID-19 pandemic, AICS outperformed both district and other charter schools across all populations, though again, for Asians the differences were not statistically significant. As Table 11 shows, we saw AICS outperforming both district schools and other charter school models in Math across all populations, generally with higher levels of significance than before the pandemic.

5. Discussion, Limitations, and Future Research

Findings highlight the academic (ELAs and math) accomplishments of academically intensive charter schools compared to district and other charter schools for nearly every grade and demographic group; these differences grew after the COVID-19 pandemic. Pre-COVID-19 data show that AICS outperformed district schools in the English Language Arts across populations, except in the case of Asian American students, where the results were no different. Post-COVID-19, AICS further widened the performance gap, with students excelling in both ELAs and mathematics across all demographic groups. These results may suggest that strong AICS cultures possess the resilience and adaptability necessary to maintain academic excellence during periods of disruption.

That being said, a key limitation is the lack of value-added data. Second, and relatedly, though the demographic profiles of AICS mirror those of the broader charter sector, it is likely that the families choosing AICS tend to prioritize high academic standards more than parents at other charters, as discussed above. This may lead some to suspicion that high test scores reflect “creaming”—that is, disproportionately recruiting and retaining the easiest to educate students. Research indicates that generally, charter schools are no more likely than traditional public schools to recruit and retain such students (Kho et al., 2022). Yet, AICS are not typical charter schools. More research is needed to determine to what degree, if any, “creaming” explains AICS’ measured academic success.

Third, by aggregating the performance data of all non-AICS charter schools, this study likely obscures the successes of other specialized models, potentially underestimating their value. Fourth, many charter school models seek outcomes that are not captured by achievement data alone. For example, some charter school parents may prioritize Career and Technical Education (CTE), art, or music, which require specialized outcome measures to determine their efficacy. We advocate for more research in order to disentangle these nuances and determine whether other academic models can perform as well as or better than AICS. Further, while the methodology employed, standardizing performance data through NAEP-adjusted Z-scores, allows for meaningful comparisons, refinements addressing state-specific disparities would strengthen future analyses.

Moreover, achievement success does not mean that AICS lack challenges suggestive of future research. The following questions remain: how do they negotiate compliance demands, specifically regarding special education and certification rules? (These played a role in BASIS’s decision to develop a private division.) How do AICS recruit and retain the right educators and students while spending less than traditional public schools? Can they continue to boast excellent academic growth while expanding? How do they navigate potential bureaucratic policy constraints that state governments impose?

The high achievement of AICS raises an additional question: could adopting higher academic standards and prioritizing academics within a school, or program within a school, enable other charter schools and district schools to improve their academic outcomes, and better serve their most motivated students? The professional cultures of AICS, which prioritize teacher expertise and academic rigor, may offer lessons for broader educational reform, though it is unclear whether other schools would seek to duplicate those cultures, or prefer to continue existing practices (Maranto & Wai, 2020). Generally, findings indicate that AICS campuses have high test scores and financial viability, making a compelling case for their incremental expansion, and for further investigation of their role in creating or at least enabling academic excellence—something which, following the COVID-19 pandemic, greater numbers of students need more than ever.