The problem

Vague grading criteria lead to inconsistent marking, frustrated students, and hours wasted writing the same feedback. Teachers often struggle to translate complex learning outcomes into clear, actionable rubrics that learners actually understand.

What it does

• Generates analytic, holistic, or single-point rubrics based on assignment briefs and learning outcomes.
• Writes specific performance descriptors for each grading band to eliminate subjective marking.
• Creates learner-friendly success criteria and "I can" statements to help students self-assess.
• Produces reusable feedback comment banks for different performance levels.
• Develops moderation notes and QA checklists to ensure fairness and consistency across multiple assessors.

Why this beats prompting it yourself

This skill follows a rigorous educational design workflow that includes accessibility reviews and fairness audits. It prevents common mistakes like using circular descriptors or "hidden" criteria, and it automatically generates the supporting materials—like peer-review checklists and moderation notes—that manual prompting often misses.

Use cases

• Designing a marking scheme for a new university module or corporate training program.
• Translating a technical grading matrix into plain language for junior learners.
• Auditing an existing rubric to fix vague language and alignment issues.
• Building a feedback library for high-volume assignment marking.

Known limitations

Cannot access or verify official exam board boundaries, accreditation standards, or specific institutional policies without user input. It does not replace professional academic judgment.