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.

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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

Six domains. One clear report.

ATQS-informed review

Course AI readiness profile

Policy clarity

Assignment fit

Assessment design

Transparency

Tool use

Instructional design

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

What gets reviewed

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 & Alignment	Is AI use tied to learning outcomes?	Outcome fit, task purpose, and whether AI use strengthens rather than distracts from the course goal.
Transparency & Expectations	Do students know what is expected?	Assignment-level guidance, disclosure expectations, and clarity around acceptable and unacceptable use.
Learning Design & Scaffolding	Is AI integrated intentionally and progressively?	Scaffolding, sequencing, and whether students are coached toward stronger judgment over time.
Authentic Assessment & Process Evidence	Does 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 Development	Does the course build evaluative AI judgment?	Verification habits, judgment, tool choice, communication, and student capability growth.
Ethics, Trust & Human Oversight	Are ethics and oversight addressed directly?	Bias, fairness, accountability, privacy, and how human judgment is retained in the course design.

How the review works

Intake and course submission

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

Structured review against the standard

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

Findings, scores, and recommendations

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

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

View self-audit

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

Institutional Review Package

For departments, schools, online learning units, or institutions building a repeatable AI course-quality process.

Multi-course review and cross-course pattern analysis

Leadership summary and stronger reporting language

Pairs naturally with cohorts, bootcamps, and broader QA work

Ask about institutional review

Course quality next step

Does your course meet the AI-ready standard?

Use CourseReady AI to review what is already in place, identify where the design is vulnerable, and build a clearer path toward more credible AI-era teaching.

Book a Discovery Call View the Rubric Guide