AI grading of the exam is highly stable across five independent runs at the total-score level (ICC(A,1) = 0.967), with lower but still strong cell-level stability (ICC(A,1) = 0.836)¶
Claim · All claims
Evidence · 1 study (2 entries) · designr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? On total scores per student, the AI showed high run-to-run consistency: ICC(A,1) = 0.967 for a single run and ICC(A,5) = 0.993 if five runs were averaged, with a 95% repeatability coefficient of 5.33 points. → Jan Cvengros and Gerd Kortemeyer 2026
q2 i? At the item×student level (rubric cells), agreement was lower yet still strong: ICC(A,1) = 0.836 and ICC(A,5) = 0.962, with Sw = 0.25 points and RC = 0.68 points per cell. → Jan Cvengros and Gerd Kortemeyer 2026 (2)
Evidence¶
Jan Cvengros and Gerd Kortemeyer 2026¶
Jan Cvengros and Gerd Kortemeyer. (2026). Assisting the grading of a handwritten general chemistry exam with artificial intelligence. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-01606-4
q2 · i? · design · r2
Inter-run reliability analysis treating five independent AI grading runs as five raters, using two-way random-effects absolute-agreement ICC on raw total scores. The article also reports Sw = 1.92 points and a 95% repeatability coefficient RC = 5.33 points, with Kendall's W = 0.959 for rank stability.
"ICC(A,1)=0 .967 for a single run and ICC(A,5) =0 .993 if one were to average five runs"
Jan Cvengros and Gerd Kortemeyer 2026 (2)¶
Jan Cvengros and Gerd Kortemeyer. (2026). Assisting the grading of a handwritten general chemistry exam with artificial intelligence. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-01606-4
q2 · i? · design · r2
Same five-run reliability analysis computed at the rubric-cell (item×student) level. The article notes cell-level jitter is small, explaining why summed totals are very stable, and cautions that with about one third of ground-truth scores at zero the ICC would be inflated.
"At the item×student level (rubric cells), agreement was lower (as expected for finer granularity), yet still strong: ICC(A,1) =0 .836 and ICC(A,5) =0 .962, with Sw =0 .25 points and RC =0 .68 points per cell"
Discussion¶
Related Claims¶
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Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ai-grading-run-to-run-reliability-icc |
title |
AI grading of the exam is highly stable across five independent runs at the total-score level (ICC(A,1) = 0.967), with lower but still strong cell-level stability (ICC(A,1) = 0.836) |
description |
AI grading of the exam is highly stable across five independent runs at the total-score level (ICC(A,1) = 0.967), with lower but still strong cell-level stability (ICC(A,1) = 0.836) |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — jan-cvengros-and-gerd-kortemeyer-2026, jan-cvengros-and-gerd-kortemeyer-2026-2 |
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