CourseReady AI

AI-ready course review built around a clear quality standard.

CourseReady AI helps institutions review whether a course is handling AI credibly across policy, assignments, assessment, transparency, tool use, and instructional design. This is not a vague “AI-friendliness” check. It is a structured review process with visible criteria, usable feedback, and a practical path for improvement.

Book a Discovery Call View a Sample Report
Useful for individual faculty, online learning teams, or institution-level review models
Generates a structured report rather than a generic page of comments
Helps faculty redesign with clearer expectations instead of fear-driven guesswork
Works well alongside faculty development, bootcamps, and broader governance or QA work
Sample review interface
AI quality standards for courses
ATQS-informed review

Course AI readiness profile

Policy clarity
81
Assignment fit
72
Assessment design
63
Transparency
76
Tool use
54
Instructional design
68

Sample findings

Strong policy signalSyllabus language is clear, but assignment-level expectations still need more specificity.
Assessment vulnerabilityTwo major assessments remain highly promptable without enough process visibility.
Recommended actionAdd reflection checkpoints, authorship explanation, and revised assignment prompts.
6review domains spanning policy, assessment, assignments, transparency, tool use, and instructional design
4review steps from intake to final report and redesign guidance
1quality standard that can be used for individual courses or scaled institutional implementation
0interest in shallow rubber-stamp badges that tell faculty nothing useful about what to improve
Campus implementation

Want to build internal review capacity — not just buy one-off reviews?

CourseReady AI is available in two ways: Navigate AI-led review for individual courses, pilots, and department packages, and a Campus License + Reviewer Training option for institutions that want to train internal reviewers, establish a shared review process, and build a repeatable AI-ready course-quality model.

ATQS reviewer training and calibration for designated internal reviewers
Institutional review process design, documentation templates, and rollout guidance
Recognition pathway setup for local review cycles and repeatable quality checks
Optional pilot reviews, annual benchmark reporting, and leadership summary support
Ask about campus licensing View campus license brief
Best fit for CTLs, online learning units, colleges, systems, and institutions expecting enough review volume to justify internal reviewer capacity.
What changes Instead of relying only on external reviews, your institution gains a shared review model, trained internal reviewers, and a more durable path for course-level AI quality assurance.
A good starting point if you're unsure: begin with a 3–6 course pilot, then move into internal reviewer training after the process is proven locally.
What gets reviewed

AI quality standards for courses across six domains.


Each one addresses a different way AI can strengthen or weaken course quality.

The goal is not to punish AI use or celebrate it automatically. The goal is to examine whether the course has made thoughtful choices about where AI belongs, where it does not, and how students will understand those boundaries.

ATQS review domains

A cleaner view of the six areas reviewers examine.

Rather than placing the full rubric on this page, this summary table gives a faster view of what a CourseReady review actually looks at.

Domain Core review question What reviewers examine
Purpose & AlignmentIs AI use tied to learning outcomes?Outcome fit, task purpose, and whether AI use strengthens rather than distracts from the course goal.
Transparency & ExpectationsDo students know what is expected?Assignment-level guidance, disclosure expectations, and clarity around acceptable and unacceptable use.
Learning Design & ScaffoldingIs AI integrated intentionally and progressively?Scaffolding, sequencing, and whether students are coached toward stronger judgment over time.
Authentic Assessment & Process EvidenceDoes the course preserve real thinking and evidence of process?Authenticity, checkpoints, reflection, drafts, oral defense, and other evidence that learning remains visible.
Student AI Fluency DevelopmentDoes the course build evaluative AI judgment?Verification habits, judgment, tool choice, communication, and student capability growth.
Ethics, Trust & Human OversightAre ethics and oversight addressed directly?Bias, fairness, accountability, privacy, and how human judgment is retained in the course design.
How the review works
01

Intake and course submission

The review begins with course materials, selected assignments, policy language, and any context needed to understand the course design choices.

02

Structured review against the standard

CourseReady AI evaluates the course across the six domains using a defined rubric and practical reviewer judgment.

03

Findings, scores, and recommendations

The course receives a report with domain-level insights, strengths, risks, and improvement recommendations.

04

Redesign support or next-step planning

Institutions can use the review as a one-off quality check or as part of a broader faculty development and quality assurance model.

What you receive A structured review report, domain scores or ratings, narrative feedback, and concrete suggestions for where to revise policy, assignments, or assessment design.
Leadership-friendly output
What faculty receive Clearer guidance about what is working, what is risky, and what specific changes would make the course more AI-ready without flattening pedagogy.
Improvement-focused
What institutions gain A more defensible language for course quality and a model that can scale beyond isolated course-level experimentation.
Scalable standard
Review options

Choose the level of review that fits the need.

CourseReady AI now works as a simple self-audit, a guided course review, or a scaled institutional review package. Each option uses the same ATQS logic — the difference is depth, reporting, and level of support.

Self-Audit Pack

For faculty who need a structured internal diagnostic before requesting a full review.

ATQS self-audit tool and guidance notes
Improvement prompts for policy, design, and assessment
Useful as pre-review preparation or workshop follow-up
Ask about the self-audit pack

Guided Course Review

For a faculty member, chair, CTL, or program lead who wants a credible outside review of a live course.

Domain-scored review across all six ATQS areas
Narrative feedback and prioritized improvement guidance
Optional AI-Ready recognition for courses above threshold
Request guided review

Campus License + Reviewer Training

For institutions that want to license the CourseReady AI process, train internal reviewers, and build a repeatable AI-ready course-quality model.

ATQS reviewer training and calibration for designated internal reviewers
Institutional review process design, recognition setup, and documentation templates
Optional pilot reviews, annual benchmark reporting, and leadership summary support
Ask about campus licensing

Frequently Asked Questions

CourseReady AI FAQs

Common questions about courseready ai from institutional leaders, faculty, and program administrators.

What is CourseReady AI?

CourseReady AI is a structured course-review process that evaluates whether a course 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 AI policy alignment, transparency and disclosure, course and assignment design, assessment logic, fluency expectations, and ethics-related considerations.

Is CourseReady AI for one course 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.

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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