Case study - Pacific Collegiate Charter School

Pacific Collegiate Charter and AI

Discover how Pacific Collegiate Charter boosts student success and saves time with an AI grading platform for math!

Pacific Collegiate School, on the map of California
Pacific Collegiate students working on their laptops
The school
Pacific Collegiate SchoolGrades 7-12 charter school
Students:
514
Location:
California
Using Ed.ai since:
September 2026
He tells us his story
Christopher GuyerHead of school

Overview and challenge

When Indiana made the SAT a graduation requirement in 2020, Hanover Community School Corporation recognized that preparation could no longer be informal or uneven.
With graduation tied directly to SAT performance, readiness became a shared district responsibility—one that required more than access to resources.

Prior to formalizing a district-wide approach, SAT preparation varied by classroom. Students had access to high-quality materials, but leadership lacked consistent cohort-level visibility. There was no unified structure to determine whether practice translated into checkpoint performance across the junior class.
Rather than assume readiness, Hanover chose to operationalize it.
District leaders elevated SAT preparation to a system-level priority. The objective was clear: prepare every junior and verify that preparation translated into measurable performance on College Board assessments.
Partnering with Khan Academy Districts as a nonprofit, mission-aligned provider, Hanover shifted from decentralized practice to a governed, district-wide SAT readiness model.

I don’t want my students to think math is just about guessing answers, points, and prizes. I want them to learn to work hard, fix mistakes, and explain their thought process.

Christopher GuyerHead of school

Solutions and Implementation

Teachers used real-time mastery data to make immediate instructional decisions. When students struggled with specific skills, educators could quickly identify those gaps and adjust instruction through targeted mini-lessons or additional practice.
Assignments were aligned directly to STAAR standards, ensuring that practice reinforced the skills students needed for upcoming assessments.
For students, this clarity made it easier to stay on track, complete assignments, and build momentum through consistent practice.
Teachers used real-time mastery data to make immediate instructional decisions. When students struggled with specific skills, educators could quickly identify those gaps and adjust instruction through targeted mini-lessons or additional practice.
Assignments were aligned directly to STAAR standards, ensuring that practice reinforced the skills students needed for upcoming assessments.
For students, this clarity made it easier to stay on track, complete assignments, and build momentum through consistent practice.

How a pilot works

Unlimited use throughout the 10 weeks. No caps on teachers, classes, or papers.

  1. Week 1
    KickoffAccess opens & teacher training for AI grading.
  2. Week 3
    Feedback sessionAI grading feedback; teacher training for insight & reteaching.
  3. Week 5
    Midpoint reviewUsage review with the pilot lead and a school representative.
  4. Week 10
    Final review & next stepsResults summary and rollout perspectives with leadership.

Results and District-reported Outcomes

  • About 40% of December STAAR retesters passed their exams, a result district leaders largely attributed to focused, structured practice during advisory.

  • Expanded implementation: Khan Academy grew from advisory use to daily classroom integration.

  • About 500 middle and high school students are actively using the platform.

  • More efficient instructional planning: One teacher reported saving approximately four to five hours per week.

  • More targeted instruction: Real-time data enabled faster identification and support of skill gaps.

What began as targeted intervention is now a broader instructional strategy
What started as a focused effort to support STAAR retesters—and a pilot of Khan Academy’s reimagined classroom experience—has expanded into a broader approach to teaching and learning across Taft ISD.
By embedding structured, data-informed practice into both advisory and classroom settings, the district is building a more consistent system for supporting student progress.
Taft’s experience offers a preview of how the reimagined Khan Academy classroom experience can support districts in delivering structured, data-informed intervention at scale.

I don’t want my students to think math is just about guessing answers, points, and prizes. I want them to learn to work hard, fix mistakes, and explain their thought process.

Christopher GuyerHead of school

Built for Math. Trusted by Math Teachers.

Join thousands of high school math educators who’ve cut grading time by 80% without sacrificing feedback quality.


Frequently asked questions

Yes. Ed.ai is fully compliant with FERPA and all applicable US student data privacy regulations.

Yes. Schools and districts can pay by purchase order. Talk to us and we will send you the quote to attach to it.

Yes. Ed.ai can be purchased with federal Title I, Title II-A and Title IV-A funds. Ask us for the documentation your business office needs.