CourseReady AI

Review the course students actually experience when AI is available.

CourseReady AI is a focused, developmental review of whether a course’s purpose, expectations, learning design, assessment evidence, student fluency, and human oversight remain coherent in an AI-enabled environment.

Discuss a CourseReady reviewSee review options →
The question behind the review

A course works under AI-enabled conditions when its purpose, expectations, learning design, and assessment still align.

A course can state what is allowed while assignments remain ambiguous, evidence of student thinking disappears, or AI use is disconnected from the learning objectives. CourseReady examines the whole learning design.

Syllabus and AI expectationsPermission, disclosure, data, and accountability
Assignments and rubricsLearning purpose, AI conditions, and process evidence
LMS guidance and scaffoldingStudent support, examples, practice, and accessibility
Student artifacts and improvement recordsWhat the course can demonstrate and how it changes
The ATQS review model

Six domains. Twenty-four criteria. One integrated course story.

The review is developmental, consent-based, and evidence-centered. It identifies what is strong, what is unclear, and which course changes will create the greatest learning value.

What changes

Turn course expectations into a defensible learning design.

The review uses the score to improve the relationship among learning objectives, AI permissions, student process, verification, and the evidence used to judge learning.

Before

“AI may be used with disclosure.”

  • No assignment-level role for AI
  • Final artifact carries most of the grade
  • Verification is encouraged but not designed
  • Student judgment remains difficult to observe
→
After

AI use aligned with the learning purpose.

  • Clear role and boundaries for the tool
  • Process evidence built into the task
  • Verification and human judgment scored
  • Students explain and defend key decisions
Ways to use CourseReady AI

Start with one course or build internal review capability.

The same framework can support individual faculty reflection, guided review, a multi-course initiative, or a calibrated internal reviewer model.

01

Faculty self-audit

A structured review and improvement plan led by the instructor.

02

Guided course review

Evidence review, faculty consultation, scoring, and a course improvement report.

03

Department or program review

A coordinated view across priority courses, policies, and assessment patterns.

04

Reviewer training and calibration

Build a sustainable internal review process with shared interpretation and governance.

What the school receives

Course-level evidence that can support broader claims.

  • Scored review across six ATQS domains and 24 criteria.
  • Evidence inventory, strengths, gaps, and priority recommendations.
  • Faculty consultation and a practical course improvement plan.
  • Redesigned policies, assignments, rubrics, and student guidance as scoped.
  • Multi-course findings and implementation themes for department reviews.
  • Reviewer calibration tools for internal adoption.

CourseReady complements rather than replaces Quality Matters or other general course-quality systems. It focuses specifically on course readiness when AI changes how students work and how learning must be evidenced.

Course evidence stack
Purpose and alignment
Why AI belongs, or does not belong, in the learning task.
Student expectations
What is permitted, required, documented, and accountable.
Assessment evidence
What makes reasoning, verification, and ownership visible.
Improvement record
What changed because the evidence was reviewed.
Common questions

What faculty and leaders usually ask.

Does CourseReady require faculty to use AI in every assignment?

No. A strong course may include AI-enabled, AI-limited, and AI-free tasks. The review asks whether each choice follows the learning purpose.

Is this an evaluation of the faculty member?

No. The review is developmental and focused on course design. Governance and consent matter, especially in multi-course initiatives.

What materials are reviewed?

Typically the syllabus, AI guidance, selected assignments and rubrics, LMS support, and available student or improvement evidence.

Can we train internal reviewers?

Yes. The complete system includes calibration materials and governance guidance for a licensed institutional model.

Make priority courses credible under the conditions students now work in.

Begin with one course, a department sample, or an internal reviewer initiative.

Discuss CourseReady AISee how it connects to the Diagnostic
Course quality next step

Does your course or program meet the AI-ready standards?

Use this form to ask about the self-audit, a guided review, or an institutional review package. Start with your context and the kind of review support you want.

Helpful detail: Tell us whether this is for one course, a small pilot, or a larger institutional review effort, and whether you are exploring self-audit, guided review, or a multi-course package.
Review options

Where most teams start

One course needs a guided outside review
A school wants a pilot with several courses
An online learning team wants a clearer AI-ready quality standard
A faculty member wants the self-audit before a full review
Prefer to browse first?

Start with the rubric guide.

The CourseReady page and rubric summary help leaders understand the review logic before requesting a call.

Direct contact

Email works too

If you already know the review level you want, email directly with your context.

Typical response time: 1–2 business days
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