New Zealand's NZQA trained an AI model on tens of thousands of past-marked scripts to grade the national NCEA Writing literacy exam, reaching 80% agreement with human markers and cutting results turnaround by 3.5 weeks for 55,000+ students in 2025.
80%
AI-human marker agreement rate (2024 trial)
36,000
Writing samples in the 2024 trial
55,000+
Students in the May 2025 national rollout
~20,000 ~40%
Scripts still checked by a human marker (May 2025)
3.5 weeks
Faster results turnaround (vs. previous year)
Details
Maturity
Established
Promoter
New Zealand Qualifications Authority (NZQA)
Period
2024–present
Keywords
government agency, national qualifications authority, secondary education, assessment
Context
New Zealand Qualifications Authority (NZQA) built an Automated Text Scoring tool, training a machine-learning model on thousands of previously human-marked scripts to grade the NCEA co-requisite Writing standard, a national literacy assessment that most secondary students must pass to gain NCEA qualifications.
Objectives
Speed up and scale reliable grading of a high-stakes national writing assessment while retaining human review for borderline cases.
Activities
The AI does the primary marking; human markers check only the most borderline scripts, and wherever AI and human scores disagree, the human mark is used. A 2024 trial covered 36,000 writing samples before the tool was rolled out nationally for the May 2025 exam sat by more than 55,000 students; about 20,000 scripts (roughly 40%, concentrated near the pass/fail boundary) were still checked by a human marker.
Results
NZQA reported an 80% agreement rate between the AI model and human markers in the 2024 trial, per chief executive Grant Klinkum. Following the 2025 national rollout, schools received results 3.5 weeks earlier than in the previous year.
Conclusions
The evidence base covers grading accuracy and turnaround time, not direct student learning outcomes. The 20% disagreement rate observed in the trial is non-trivial for a high-stakes qualification gate, which is why NZQA retained mandatory human review for borderline cases rather than fully automating the exam.
Implementation
Indicative cost
Medium (€50k–€500k) — A government qualifications-authority machine-learning system trained on historical marked scripts; no public budget figure was found, but building and validating an ML marking model at national-exam scale implies meaningful upfront investment.
Time to results
Medium (1–3 years) — The 2024 trial (36,000 scripts) preceded the May 2025 national rollout for 55,000+ students, delivering results 3.5 weeks faster than the prior year.
Staffing & skills
New Zealand Qualifications Authority (NZQA) — system ownership and human-marker retention for borderline cases
Conditions for success
Training the model on thousands of previously human-marked scripts before deployment
Retaining mandatory human review for borderline scripts (about 40% of scripts) rather than fully automating
Defaulting to the human mark whenever AI and human scores disagree
Publicly disclosing trial sample size and agreement rate before national rollout
Common failure modes
A 20% disagreement rate between AI and human markers in the 2024 trial is non-trivial for a high-stakes qualification gate
Evidence covers grading accuracy and turnaround only, not direct student learning outcomes
No published subgroup equity analysis of the AI marking's effects
Commonly funded by
National / regional programmes
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
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Data sources
Where this practice's information was retrieved from, and when.
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