Strengthen the part of the educational system that matters next.
Navigate AI is organized around a simple sequence. First, establish what the school has built and what the evidence supports. Then address the specific gap that matters most. The next move may sit in student learning, a priority course, a direct measure, faculty implementation, or the improvement cycle.
Schools can begin with the AI Capability & Evidence Diagnostic or enter through a focused engagement when the problem is already clear.
Review maturity, evidence strength, and evidence lag.
Improve course quality and direct assessment when AI is available.
Clarify student expectations and strengthen faculty implementation.
Refresh the evidence and document year-over-year movement.
Begin with the evidence behind the problem.
AI strategy conversations can expand quickly. AI strategy conversations spread quickly. Governance and infrastructure questions can pull attention in one direction while curriculum, assessment, faculty practice, and student readiness pull it in another. Navigate AI uses evidence to narrow the field.
If the school already has meaningful activity, the first task is to determine where capability is strong, where the evidence is thin, and where a learning or implementation problem is still unresolved. That prevents leadership from investing in another broad initiative when a more targeted intervention would do more good.
The solutions below are designed to work independently or as follow-on engagements from the Diagnostic.
What should we do about AI?
- Can produce a long priority list
- Often crosses many institutional functions
- May require choices before evidence is organized
What does the evidence say needs attention?
- Start with a defined educational problem
- Choose the intervention tied to that problem
- Set evidence targets before the work begins
When AI changes the artifact, strengthen the inference.
These engagements focus on the places where AI makes it harder to determine what students understand, how they reasoned, and whether a course or direct measure is still producing trustworthy evidence.
Make student judgment visible under AI-enabled conditions.
Start with a direct measure the program already cares about and clarify the inference it needs to support. The sprint redesigns the task around the inference the program needs to make. It adds the process evidence required to interpret the final artifact, strengthens the rubric, and establishes a pilot and reassessment plan.
This is especially useful when AI can produce a polished memo, analysis, presentation, or recommendation that no longer reveals enough about how the student arrived there.
Strengthen assessment evidence →Review whether a priority course still produces the learning evidence it should.
CourseReady AI examines how a course functions when students have routine access to generative and agentic tools. The review begins with the purpose of the course, then examines how AI expectations shape learning activity and assessment evidence. It also looks at verification, responsible use, and where human oversight remains necessary.
Schools can use it for a single high-priority course, a small program review, or a calibrated faculty-review initiative. A full institutional Diagnostic is helpful when the broader picture is unclear, but it is not required.
Review CourseReady AI →Turn expectations into curriculum and practice.
When the evidence shows that the central need is capability development, Navigate AI helps the school define what students should demonstrate and strengthen the faculty practice required to support it.
Turn student AI capability expectations into progression and observable evidence.
The AI-Ready Graduate Framework provides a shared Human+AI Fluency language across seven domains. Programs then translate those capabilities into developmental progression, curriculum placement, and evidence that students can actually produce. The value comes from what the school can build with the framework rather than from the framework label alone.
Implementation can range from a focused capability-and-curriculum mapping sprint to a larger pathway using the existing studio sequence and student evidence portfolio.
Explore AI-Ready Graduate →Build faculty capability where the evidence shows a need.
The Faculty Studio is a targeted implementation option for schools that need stronger course practice, shared expectations, or faculty leadership. Navigate AI adapts the program to readiness, discipline, course context, and the artifacts the institution needs faculty to produce.
The underlying FAFI model and course library support differentiated development from foundational practice through leadership. The public engagement remains flexible so schools can focus on the faculty problem they actually have.
Design a Faculty Studio →Keep the evidence current after the first engagement.
AI capability will continue to change as tools, expectations, courses, and student practices evolve. The annual review gives leadership a recurring way to judge movement without restarting the entire process.
Document what changed and set the next improvement cycle.
The annual review refreshes the original maturity and evidence ratings, updates the evidence inventory, examines movement against the prior roadmap, and incorporates new course, curriculum, faculty, and direct-learning evidence.
The result is a year-over-year record that leadership can use for internal planning and quality conversations, along with a revised set of priorities for the next cycle.
See the Annual Review →Match the engagement to the decision leadership needs to make.
You do not need to buy the entire system. A school can begin with a narrow problem, provided the scope and evidence target are clear.
A short briefing can clarify the right scope.
Share the decision in front of leadership, the evidence already available, and the part of the educational system creating the most uncertainty. We will help identify a sensible first engagement.