Background/Context: There were over 500,000 open computing jobs across the country in 2018, but only 35% of U.S. high schools offer computer science classes, and only 8-10% of STEM graduates study computer science (Code.org, 2018). In 2021, only 5.1% of all Bachelor's degrees conferred were in Computer and Information Sciences (IPEDS, 2018). STEM is the future, but the present pipeline is insufficient to meet demand. K-12 Computer Science course participation is the leading indicator of what happens in university. The College Board found that students who try AP Computer Science courses in high school are 6 times more likely to study Computer Science in college, and participation is 7 to 10 times higher for minority and female students, respectively (Wyatt, Feng, & Ewing, 2020). Purpose/Objective Research Questions: This study evaluates the impact of an intervention that integrates increased access to Computer Science Principles (CSP) with curriculum and professional development support for CSP teachers. Specifically, we examine how the design of a computer science-integrated math curriculum, the Beauty and Joy of Computing (BJC), affects students' participation and performance in computer science in high school and in college. Sharing this knowledge will contribute significantly to the field, helping other secondary schools improve STEM preparation for underrepresented students nationwide. We aim to address three questions: what is the impact of enrollment in BJC Advanced Placement (AP) CSP curriculum on: 1) student AP CSP test scores, 2) student computer science attitudes and engagement, and 3) AP CSP enrollment at the school? Setting: This intervention was implemented at IDEA Public Schools, a network of charter schools based in Texas. Since 2000, IDEA has grown from a single campus serving 150 students in the Rio Grande Valley to its current enrollment of 44,653 students across 79 schools in two states and is the fastest- growing network of tuition-free, Pre-K-12 public charter schools in the United States. In spring 2021, IDEA high schools were randomly assigned to one of two AP CSP curricula in the 2021-2022 academic year, 15 schools to receive Code.org, the usual curriculum, and 15 schools to receive the Beauty and Joy of Computing (BJC). Population/Participants/Subjects: IDEA's current student population across all schools is 89.2% Hispanic, 5.5% African American, 88.6% economically disadvantaged, and 32.9% English-language learners. Additionally, 45.9% are considered at-risk for dropping out of school due to multiple academic, social, and economic factors. Our target population is comprised of students, between the 9th and 12th grade, who requested to take the AP CSP course in the spring before randomization, students who did not make a spring course request, even if they enrolled into the course later, are not included in the evaluation. Intervention/Program/Practice: After randomization, teachers in schools that were assigned the BJC curriculum participated in a one- week training at the beginning of the school year. Throughout the year, teachers attended bi-weekly webinars facilitated by the CS curriculum manager at each school. Teachers were also able to collaborate and communicate through online communication channels. Teachers primarily taught with their school's adopted materials in fall 2021, but frequently used other supplementary materials in spring of 2021. Research Design: This study utilizes a Randomized Controlled Trail (RCT) design to estimate the impacts of the BJC AP CSP curriculum. Specifically, we first conduct school-level randomization blocks and then conduct a constrained randomization to balance covariates. This is to ensure that the treated and comparison schools are statistically similar at baseline. The school blocks are created based on schools' prior history of offering an AP CSP curriculum. We then implement procedures described by Morgan and Rubin (2012) to further constrain the set of possible randomizations and achieve optimal balance for two key continuous covariates: (a) prior mathematics standardized achievement and (b) prior overall grade point average (GPA). Based on the district's historical data, these two covariates emerged as the strongest predictors of our key outcome (i.e., AP CSP scores). Our main impact model is a two-level mixed-effects model estimating treatment-control differences for continuous AP CSP scores, controlling for baseline covariates and fixed effects of randomization blocks. We also conduct exploratory subgroup analyses based on student-level characteristics, such as student grade level, gender, or prior mathematics achievement. Data Collection in Analysis: We collect administrative records from IDEA public schools that include information on student enrollment, demographic characteristics, student test scores, as well as school-level demographics and prior achievement. We also conduct a survey in both the fall of 2021 and spring of 2022. In the survey, we collect data from students on their computer science engagement, computer science confidence and interests, and computer science postsecondary and career intentions. Findings/Results: About half of students initially enrolled in spring 2021 did not remain in the course or otherwise did not take the AP CSP test in spring 2022. Our baseline equivalence analyses focus on student who did remain in the AP CSP course (i.e., those with AP CSP test scores as outcomes). The main concern with attrition is that different types of students might have left the BJC and Code.org conditions, however, findings from our baseline equivalence tests provide evidence that the two groups overall remain comparable. Also, where there are some slight differences (e.g., limited English proficiency rates), we statistically control for them by including those variables as covariates in our regression model. The average AP CSP scores were low in both conditions (compared to the national average of 2.91). About one-quarter of test takers (25%) earned a passing score of 3 or higher (compared to national average of 64%). The results of our main model show that there were no statistically significant differences between the BJC and Code.org groups, nor do we find statistically significant differences for subgroups. Although the study survey response rates were low, the results of analyzing the survey data show that students in the treatment group responded more favorably on all student engagement constructs than comparison students, the size of the differences between the treatment and comparison condition were small and none were statistically significant. There were also no statistically significant differences for CS interests, confidence, or postsecondary and career intentions.