Inclusive AI StrategiesA redesigned assignment
One assignment from their own course, redesigned, with a written rationale for the changes.
Cohort Program
AI Course Design Cohort
For online and brick-and-mortar schools, course providers and districts with course teams. Your subject matter experts and instructional designers spend about two months redesigning their own courses with AI, and they finish with course work they can show.
Who it's for
Teams that build and revise courses: subject matter experts, instructional designers, media producers and the leads who review their work. It fits best when people are already experimenting with AI on their own and leadership wants everyone working from the same practices and the same policy.
Experience levels can be mixed. In the last cohort, a few participants had never opened the AI tool before the first session.
What participants make
Each person produces three pieces of work, all from courses already on their plate.
A review workflow for their role
Which checks they run, in what order, and where the findings go, written so a colleague in the same role could follow it.
A prompt library
Prompts or reusable instructions they keep using after the cohort ends.
At the closing showcase, each person shows one piece: the before, the after, what the AI got wrong and how they caught it.
How the sessions run
Working sessions of 60 to 90 minutes, each built around participants' own courses. A typical cohort covers these topics, adjusted to each team.
- Working with the AI tool. Setup, context files, and telling the tool who you are and who you're building for.
- Alignment mapping. A gap analysis of a unit against its standards.
- Assessment design. Assignments that protect academic integrity by design instead of by policing.
- Content review, one lens per pass. Standards, accessibility, clarity, Universal Design for Learning and representation, each checked separately.
- The build layer. Interactives, quizzes and the hand-off between subject matter experts and instructional designers.
- Governance in practice. Real cases checked against your organization's own AI policy.
- Showcase.
Between sessions there are office hours and separate tracks for subject matter experts, instructional designers, media and leads. Every resource lives in an online course that your team keeps as an export when the cohort ends. The cohort works with the AI tools your organization has approved.
Before the cohort, for leadership
An optional leadership phase sets the ground rules before the first session: a readiness diagnostic, an intake with your leadership team, and an AI decision guide built from your own policy. Participants then work to your rules from day one, and leadership knows what to expect from the work.
How we measure change
Change is measured on a set schedule.
- A short survey in the pre-work sets a baseline.
- A midpoint check shows what needs adjusting while there's still time.
- At the close, the same questions return, along with a look back at where people started.
- In a session, people from different teams rate the same assignment on their own, which shows whether they now judge AI use by the same criteria.
- Ninety days later, a check shows which workflows and prompt libraries are still in use.
Leadership gets the results as group patterns, not individual scores.
After the cohort
Three check-ins over the following months, one with leadership and two in an office-hours format for course teams, with email support in between.
What it looked like for one online school
At the closing showcase, twelve subject matter experts showed before-and-afters across math, science, English, history, economics, psychology, law and world languages. Low-level vocabulary work became interactive practice, a scavenger hunt became a source-verification assignment with its own rubric, and a law course gained a case file that runs the whole term.
Talk about a cohort for your team
Each cohort is scoped to your courses, your tools and your policy. Start with a 30-minute conversation.