AI Scoring Accuracy: Method, Calibration and Limits
Our AI gives a practice estimate as a range, such as 8–9 on the CELPIP scale, never a single score, and quotes the exact words behind it. We have not published accuracy figures yet. After our first calibration round with trained raters, we will report sample size, average error and ±1-level agreement here.

Published results
Accuracy figures
Not yet published
We'll publish these after our first calibration round with trained raters. Until then, no accuracy figure appears anywhere on this site.
Each figure will also be broken down by task, recording source and first-language background.
- Sample size
- Not yet publishedanswers scored by trained raters
- Mean absolute error
- Not yet publishedaverage gap, in CELPIP levels
- Within ±1 level
- Not yet publishedshare of answers within one level
- Repeat spread
- Not yet publishedsame answer, scored twice
- Last calibrated
- Not yet publishedand the model version used
Can I trust an AI CELPIP score?
Use it as a study signal, not a prediction. An AI estimate is most useful for spotting your weakest criterion and checking whether a change helps. It can be off by a level, especially on pronunciation and on short answers, so we always show a range, the evidence and our confidence — and never call it official.
Ways to get the most from it:
- Compare several answers, not one. A pattern across three attempts says more than any single estimate.
- Watch the criterion that comes out lowest each time — that is where practice pays off.
- Read the quoted evidence. If a quote does not match what you meant, give that estimate less weight.
- Before booking the test, combine AI estimates with full timed practice, including the official CELPIP practice tests.
How does AI estimate a level from a recording?
We transcribe your recording with word-level timings, measure pace, pauses, fillers and time used in code, then ask a language model to rate the four CELPIP criteria from the audio, the transcript and those measurements. Every quote it cites is checked against your own words, and the recording is deleted after scoring.
Transcribe with timings
Speech-to-text keeps fillers such as “um” and gives every word a start and end time.
Measure delivery in code
Words per minute, pauses of 0.6 s or more, long pauses of 1.5 s or more, fillers per minute and the share of the response time you used are calculated — not guessed by the model.
Rate four criteria as a range
The model rates content and coherence, vocabulary, listenability and task fulfillment on the 3–12 scale, as a range one or two levels wide, against a checklist for the task. Writing is rated on readability instead of listenability.
Check every quote
Each piece of evidence must appear word for word in your answer. Anything that does not is removed before you see the report.
Apply fixed rules
Answers that use less than 40% of the time or have fewer than 50 words are capped at level 6. If the audio itself could not be analysed, the report says pronunciation was judged from the text only.
Discard the recording
Audio is processed in memory and never stored. Only the transcript and the report are kept — visible to you alone, until you delete them.
What does each criterion look at?
| Criterion | What we look at | Based on |
|---|---|---|
| Content & coherence | Ideas, how they are organised and connected, supporting detail | Transcript or essay |
| Vocabulary | Range, precision and natural word choice | Transcript or essay |
| Listenability (speaking) | Rhythm, pausing, fillers, pronunciation, intonation, grammar control | Audio and measured delivery |
| Readability (writing) | Grammar and sentence control, spelling, punctuation | Essay |
| Task fulfillment | Every part of the task, tone for the listener or reader, use of the time or word range | Task checklist and timing |
How will we calibrate it?
Calibration means comparing our estimates with levels that trained human raters give the same answers.
- Answers: practice recordings and essays from volunteers who opt in separately — covering all eight speaking tasks and both writing tasks, recorder and uploaded audio, and a range of first-language backgrounds.
- Raters: teachers with CELPIP or TESL teaching experience score each answer independently, without seeing our estimate.
- Fixed settings: the model, prompt version and settings are frozen before the comparison, so the figures describe the scoring you actually get.
- Consent: answers are used for calibration only with separate, opt-in consent, which can be withdrawn at any time.
What will we publish?
| Figure | What it tells you |
|---|---|
| Sample size | How many answers were compared, by task, recording source and first-language background |
| Mean absolute error | The average gap between our estimate and the raters, in CELPIP levels |
| Within ±1 level | The share of answers where our estimate was within one level of the raters |
| Repeat spread | How much the estimate for the same answer moves when it is scored again |
| Date and version | Which version of the scoring the figures apply to |
Status: data collection in progress
No accuracy figures are published yet, and none will appear anywhere on this site until they exist. The first round is planned for December 2026; this page will be updated with the results and the date.
What can’t AI judge well?
- Pronunciation in detail. It hears overall clarity, rhythm and intonation, not individual sounds the way a dedicated pronunciation engine would.
- Every accent equally. Speech-to-text makes more mistakes with some accents, which can pull an estimate down. That is why we will report results by first-language background.
- What a transcript hides. Automatic transcripts can smooth over errors or add punctuation you did not speak.
- Very short or off-topic answers. These get low confidence and capped levels.
- Memorised templates. We flag answers that sound rehearsed and generic, but this detection is approximate.
- Your official result. Real CELPIP raters, test-day conditions and the full four-skill test all differ from one practice answer.
Changelog
| Date | Change |
|---|---|
| 2026-10-19 | Method published. Scoring versions spk-v1 and wrt-v1: transcription with word timings, four-criterion ranges, verbatim evidence check. No accuracy figures yet. |
Questions people ask
Why a range instead of one number?
Is my answer used to train the AI?
Why can two attempts at a similar answer score differently?
What if an estimate looks wrong?
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Unofficial practice and estimates — not affiliated with Paragon or IRCC, and not legal advice. Read the disclaimer