AI Training Content
Quality Control System
A structured human-review system for learning design, assessment integrity, evidence quality and release readiness.
AI-assisted training can look polished and still contain consequential quality gaps.
Use a repeatable QA process to identify instructional misalignment, assessment weaknesses, unsupported claims and unresolved release conditions before learners see the material.
- Five QA domains: ALIGN, VERIFY, ASSESS, EXPERIENCE and RELEASE
- 50 scored controls and 100-point master scorecard
- Critical controls that cannot be hidden by a high average
- Alignment and cognitive-demand matrix
- Assessment Integrity Review worksheet
- Evidence and source-verification worksheet
- Issue log, human review record and final release checklist
- READY / REVISE / HOLD release recommendation
1 named professional user. One-time purchase for individual professional use.
Buy individual licence →Up to 5 named professional users within the same organisation. One-time purchase for internal professional use.
Buy team licence →Secure checkout via Stripe. Your download becomes available after Stripe confirms payment. Applicable tax treatment is shown at checkout.
Need more than 5 users? Contact hello@qualidact.com for organisation licensing.
Review the learning asset from design to release.
Instructional alignment
Check whether intended outcomes, learning activities, content and assessment work toward the same performance.
Evidence and sources
Check factual integrity, source traceability and where source or human verification is still required.
Assessment integrity
Check whether assessment provides valid, defensible evidence of the intended learning or performance outcome.
Learner usability
Review clarity, cognitive load, redundancy, duration and practical usability for the intended learner.
Human release control
Check ownership, issue closure, traceability and readiness before learner access or publication.
Why the review is structured this way.
The ALIGN controls use the logic of Constructive Alignment: intended outcomes, learning activities and assessment should work toward the same performance. Cognitive-demand checks use the revised Bloom taxonomy as a practical reference. Assessment controls also reflect established item-writing principles.
Qualidact operationalises these principles into its own QA workflow. It is not a certification or a guarantee of learning effectiveness.
Example: a hidden alignment gap
Objective: Apply the complaint-handling procedure to a customer case.
Activity: Read the policy only.
Assessment: Define escalation.
Finding: The intended outcome requires application, but activity and assessment remain below that level.
High scores cannot hide critical failures.
READY
No critical failures and no unresolved material verification. Proceed to the authorised human release decision.
REVISE
No critical failures. Correct identified findings and re-check the affected controls.
Do not release yet
Below 75%, any critical failure, or unresolved material verification that could affect accuracy, safety, validity or compliance.
Designed for professional learning teams.
Use it for modules, courses, facilitator guides, job aids, quizzes, assessments and other learning assets created fully or partly with generative AI.
- Instructional designers
- L&D specialists and managers
- Training developers
- Learning-content producers
- SMEs reviewing training
- Small teams without a dedicated QA function
Can't I just ask ChatGPT to review my training?
You can ask a general-purpose model for feedback. This product gives you a repeatable review structure, explicit evidence and verification logic, critical controls, assessment checks, a scorecard and a documented human release process. It is a method and working toolkit, not an AI prompt pack.
Does Qualidact guarantee that the training works?
No. The system reviews design quality and release readiness. It cannot prove learning effectiveness without evidence from actual delivery and learner performance.
Does it guarantee factual or legal compliance?
No. Material claims may require approved sources, qualified SMEs or specialist review. High-consequence domains still require authoritative human oversight.
Can I use it without AI?
Yes. Many controls are useful for any learning-content review, although the workflow is designed around risks introduced or amplified by AI-assisted production.
What format will I receive?
A professionally formatted PDF designed for repeated working use.