What is CourseReady AI?

CourseReady AI is a structured course-review process that evaluates whether a course meets AI course quality standards and is handling AI credibly across six domains: policy, assignments, assessment, transparency, tool use, and instructional design.

What does a CourseReady AI review evaluate?

The review examines the course against AI course quality standarsd, examining AI policy alignment, transparency and disclosure, course and assignment design, assessment logic, fluency expectations, ethics-related considerations, etc.

Is CourseReady AI for one ourse or a program-wide review?

It can be used for a single course, a pilot across several courses, or broader review models for departments, schools, and online learning teams.

What does an institution receive after a CourseReady AI review?

Typical outputs include a structured feedback report, improvement priorities, specific recommendations by domain, and a clearer pathway for strengthening AI-ready course quality.

CourseReady AI

See whether course design still produces credible evidence of learning when AI is available.

CourseReady AI is a focused, developmental review of the course students actually experience. It examines how learning goals, AI expectations, assignments, assessment evidence, student capability, and human judgment work together under real AI-enabled conditions.

Discuss a CourseReady review See review options →

Why course-level evidence matters

Institutional AI plans become real inside individual courses.

A school can define strong policies and student capability expectations at the program level, yet students encounter those decisions one assignment at a time. CourseReady AI looks closely at that implementation layer.

Faculty may be thoughtful about AI while students still receive uneven guidance across assignments. A syllabus may explain what is permitted without showing how AI supports the learning objective. A final project may look excellent while providing very little visibility into the student’s reasoning, verification, or independent judgment.

Those are course-design questions rather than simple policy questions. CourseReady gives faculty and academic leaders a common way to examine them, document what is already working, and focus improvement on the parts of the course that most affect learning evidence.

Syllabus and AI expectationsWhat students are told about purpose, permission, disclosure, data, and accountability

Assignments and rubricsHow AI conditions connect to learning goals and what the grading criteria actually reward

LMS guidance and scaffoldingHow students learn to use, question, verify, and make decisions about AI outputs

Student and improvement evidenceWhat the course can demonstrate about learning and how findings lead to revision

The ATQS review model

Six domains examine the complete AI-enabled course.

The AI Teaching Quality Standard uses 24 criteria across six domains. The review looks for coherence across the course rather than rewarding the number of AI tools used.

Review domain	The question CourseReady asks
Purpose & alignment	Does each AI condition support the learning purpose, and are the expectations for independent human work clear?
Transparency & expectations	Can students tell what AI use is allowed, limited, required, or prohibited for the work in front of them?
Learning design & scaffolding	Does the course help students develop capability progressively rather than encounter AI as an isolated activity?
Authentic assessment & evidence	Does the assessment capture enough evidence to interpret the student’s own reasoning and performance?
Student AI fluency development	Are students learning to evaluate outputs, verify claims, explain choices, and use AI with appropriate judgment?
Ethics, trust & human oversight	Do course design and instructor guidance make responsibility, boundaries, and human accountability visible?

What the review changes

Move from AI permission language to an evidence-rich learning design.

The practical value comes from reconnecting the learning objective, the role of AI, the student process, and the evidence used to judge performance.

Before

“AI may be used with disclosure.”

The role of AI changes from assignment to assignment without enough guidance.
The polished final artifact carries most of the assessment weight.
Verification is encouraged but remains largely invisible.
Faculty have limited evidence of the student’s judgment and decision process.

→

After

AI conditions follow the learning purpose.

Each task establishes a clear role and boundary for AI.
Relevant process evidence is built into the assignment.
Verification and human judgment appear in the rubric.
Students explain or defend decisions when the learning outcome requires it.

Ways to use CourseReady AI

Begin with one course or review a strategic set.

CourseReady can stand on its own when a school already knows where it wants to focus. It can also follow the AI Capability & Evidence Diagnostic when course implementation or direct learning evidence emerges as a priority.

Faculty self-audit

An instructor uses the framework to identify strengths, questions, and a focused improvement plan before or between formal reviews.

Guided course review

Navigate AI reviews the evidence, consults with the faculty member, scores the course, and produces prioritized recommendations.

Program sample

A coordinated review of several priority courses reveals patterns that cannot be seen from one course alone.

Internal review capability

Reviewer training and calibration can help a school use the framework consistently as part of its own improvement process.

What you leave with

Course-level findings faculty and leaders can act on.

A guided review produces more than a score. It creates a documented account of the course’s current design, the evidence behind the rating, and the changes most likely to improve learning quality.

Evidence-based review

Scoring across all six ATQS domains, with specific evidence references, strengths, gaps, and the reasoning behind priority findings.

Faculty improvement plan

Practical recommendations for policies, assignments, rubrics, scaffolding, process evidence, verification, or student guidance as the scope requires.

Leadership-level insight

Multi-course engagements summarize recurring implementation patterns so program leaders can distinguish individual course issues from broader support needs.

CourseReady complements general course-quality systems such as Quality Matters. Its purpose is narrower: to examine whether the design remains coherent when AI changes how students complete work and how faculty must interpret evidence of learning.

Typical investment

Use the smallest scope that answers the quality question.

A single guided review offers a low-friction entry point. Multi-course work becomes useful when leadership needs to see whether similar evidence or design issues appear across a program.

Single guided course review

$2,500–$3,500

A full developmental review of one priority course.

Evidence review and scoring
Faculty consultation
Written improvement report

Three-course program review

$6,500–$8,500

A coordinated review designed to identify course-level strengths and recurring program patterns.

Three complete reviews
Cross-course findings
Leadership summary

Department or reviewer initiative

From $10,000

For broader review, internal reviewer development, calibration, or a larger strategic course sample.

Scope matched to course count
Calibration and governance options
Institutional reporting as needed

Ranges are indicative and finalized after scope. CourseReady is developmental and should not be used as a personnel evaluation mechanism without separate institutional governance and consent.

Common questions

What faculty and academic leaders usually want to know.

Does an AI-ready course need to use AI in every assignment?

No. A strong course may include AI-enabled, AI-limited, and AI-free work. The review asks whether each condition follows the learning purpose and whether students understand why the condition exists.

Is this a faculty evaluation?

The standard is designed for developmental course review and improvement. Multi-course or institutional use should establish clear consent, access, governance, and reporting rules before reviews begin.

What evidence is usually reviewed?

Most reviews use the syllabus, AI guidance, selected assignments and rubrics, relevant LMS support, and available student or improvement evidence. The scope stays focused on artifacts that can answer the review questions.

Can a school use CourseReady without the full institutional Diagnostic?

Yes. CourseReady is a standalone service when the course-level problem is already clear. It also works as a targeted follow-on when the Diagnostic identifies uneven course implementation or weak direct learning evidence.

COURSE-LEVEL EVIDENCE

Start with a course where the learning question matters.

A single review can show whether CourseReady gives your faculty and leadership the kind of evidence they need before expanding to a broader program sample.

Request a CourseReady briefing Explore the institutional Diagnostic