How CIMA case study marking works, and how we check it

How your answer is marked.

• We follow CIMA's published 2026–27 marking approach. Each requirement is split into the points a marker rewards, and each point is placed at Level 1, 2 or 3 by best fit, then given a mark inside that level's range. A level is judged on technical understanding, communication, use of the information and application to the scenario. Marks are given for what you show, never taken away. You see the level and marks for each point, a score for each of the four elements, and your result by core activity, as a practice estimate out of 150 with the pass line at 80.
• Every answer is first marked automatically by AI against this scheme. You can query any mark: an independent second marking looks at it first, then a CIMA-qualified (CGMA) moderator if it doesn't settle.
• It is given the task, its requirements, your pre-seen company and, where the task has one, a marking guide. It is told to judge how well you answered the question set and applied it to the company.
• You see a scaled estimate on a 0–150 scale with the pass line at 80, like CIMA's results, with your raw marks beside it, plus strengths, the points to improve and the biggest win. Your raw marks are converted to a scaled score out of 150, with 80 as the pass line. CIMA sets its own conversion for each exam and doesn't publish it, so our scaled figure is an estimate.
• If you think a mark is wrong, use “Think this mark is wrong?” on your feedback. An independent second marking looks at it first, and a CIMA-qualified (CGMA) moderator checks any query that doesn't settle, or that you still disagree with. If the mark changes you see the new mark, the date and the reason.

Our marks follow CIMA's published 2026–27 marking approach. They are a practice estimate to help you improve, not an official CIMA mark or a prediction of your result.

How we check it.

Qualified markers mark a sample of real student answers on their own, and we record their mark next to the AI's. For every answer they mark, we compare the two on the same 0–150 scale:

• Average difference: how many points apart the two marks are, on average.
• Within 10 points: the share of answers where the marks are 10 points or less apart.
• Same side of 80: the share where both say pass, or both say not yet. This matters most, because it's what tells you whether you're ready.

Some marks are entered blind: the marker records their mark before seeing ours. Only blind marks are used to settle the scale, so our own mark can't sway the check.

If a reviewer has changed a mark after a query, we compare the AI's original mark, not the corrected one. We publish the figures once at least 30 answers have a human mark, and show them as they are, good or bad.

What we've found.

Study under way: figures coming

We'll publish the agreement figures here once 30 answers have been checked by a qualified human marker. A smaller sample could make the marking look better or worse than it is.

Until then, treat the AI mark as a guide to what to improve, and look at the pattern across several answers rather than any single score.

The current marking.

Answers are now marked point by point: the marker lists each point you made and its level, and fixed rules turn those points into marks the same way every time. Where two markers mark an answer independently and a senior check settles any difference, we call it the marking panel.

We'll add figures for answers marked this way once 30 of them have been checked by a qualified human marker.

How settled the scale is

Raw marks become an estimate out of 150 through a calibration factor for each level. We set it by comparing our marks with qualified markers' marks of the same answers. Until at least 3 of those comparisons for a level were marked blind, the factor is provisional: the estimate and the chance of passing are a rough guide, and may move as more checks come in.

• Operational Case Study: Not calibrated yet
• Management Case Study: Not calibrated yet
• Strategic Case Study: Provisional