RJUHSD Ascent
Applied AI build cohort
A small, cross-role cohort. Bring recurring work, research and shape it, build and test something useful, then share what can scale.
Roseville piloted this work with district leaders, and the clearest result wasn't the tools. It was what each person could do next. The Ascent extends that same growth to a small, cross-functional class of people running our district, sites, and operations: real capability, built on your own work, with the time it saves going back to students.
No generic training. Every session works on a tool you actually need, using district workflows. By the end of each session, something useful is ready to put to work. The proof is useful work, not seat time.
Time back from the work that drains it goes back to students. Faster board reporting, cleaner data, less time on forms and formatting, more time on the decisions that matter.
The capability grows through repeated practice. You leave able to make the next useful thing, explain the method, and help the work travel beyond one person or team.
Each working session moves a real piece of district work forward. Research the problem, build and test the response, then make the result understandable enough to share and improve.
Map the recurring work, the people involved, the source material, and the friction worth solving. Start with the work as it actually happens.
A precise problem is the first build decision.
Turn the problem into a working dashboard, presentation, application, or workflow. Test it with the people and conditions it needs to serve.
The work becomes useful through evidence and revision.
Make the result visible, explain what changed, and identify what another site or team would need to adapt it responsibly.
The useful pattern matters as much as the finished artifact.
Co-led by Amy Black and Josh Mason of RJUHSD and Kevin Bice of Karst, building on Roseville's first wave of applied AI work.
Begin with a real workflow. Build something people can open, use, and improve. Choose a category to explore the quality and depth participants can build toward. Dashboard and workflow scenarios are synthetic; the presentation viewer uses a public RJUHSD source artifact.
A synthetic operating view joining priorities, dependencies, decisions, and follow-through.
The read: seven items are ready for decision; the only shared bottleneck is ownership on two cross-team dependencies.
DEMO-1047 · District Services · Operations · added to the local coordination queue.
Opening the workflow desk…
The submitted values now drive the first queue row, the counts, and the workload read.
Your request: the sample record landed in Operations with a This week priority; the queue and category count moved with it.
RJUHSD-styled prototypes · Dashboard and workflow data are synthetic · Presentation content is sourced from a public LCAP artifact
Participation is voluntary and selected across roles and sites. District leaders, site administrators, coaches, TOSAs, and other leaders work together so useful ideas can travel across the system.
People leading programs, services, data, and strategy who want to turn recurring work into clearer, more usable systems.
Site leaders building dashboards, communications, and workflows around the work schools actually need to manage.
Cross-role builders who help practices move between classrooms, sites, departments, and district teams.
The Karst AI Leader Track is a proposed applied pathway for people leading real work with AI. This section remains private until RJUHSD approves the public standard.
A documented AI workflow operating in your actual role.
Three or more tools in use at your site or department.
One session where colleagues learn the method from you.
A capstone shared with district colleagues.
Track 01 · Applied Practice
The Karst AI Leader credential is a proprietary professional-learning designation. It does not confer academic credit, professional licensure, or third-party accreditation, and it does not represent a determination that any person, tool, or system is safe or compliant.
The cohort is intentionally small and selected across roles and sites. Tell the review team who you are and which recurring piece of work you want to make better.
You will identify your role and site, describe one recurring piece of work worth improving, and confirm the participation, technology, and information boundaries.
Open the Application