NZQA Automated Text Scoring — AI Marking of New Zealand's National Writing Literacy Standard
New Zealand
New Zealand's NZQA trained an AI model on tens of thousands of past-marked scripts to grade the national NCEA Writing …
Indonesia · Kuningan · See the Indonesia profile
Evidence: Observational / pre–post Top 81% 33/100 · Ask Evidence Copilot about this practice
Universitas Kuningan researchers built and validated AKM Online, an AI-enhanced version of Indonesia's national competency assessment, testing 552 students across three schools with 94.35% AI-scoring accuracy on 17,049 responses.
Indonesia's national Minimum Competency Assessment (AKM) is the government's standard tool for measuring foundational literacy and numeracy skills, but paper- and generic computer-based versions struggle to deliver reliable, real-time diagnostics at scale. Researchers at Universitas Kuningan developed and validated AKM Online, an AI-enhanced Computer-Based Assessment system.
AKM Online integrates Fisher-Yates Shuffle and Regular Expression algorithms for secure test randomisation, automated response validation, and real-time diagnostic feedback. It was trialled across three schools chosen for contrasting socioeconomic contexts.
The trial generated 552 student participants and 17,049 individual responses. The AI scoring engine reached 94.35% accuracy against expert human grading, and independent expert panels rated system quality at 81% for educational-assessment design and 82% for informatics performance. Diagnostics found 95.8-100% of students needed intervention in numeracy, 53.9-78.46% needed intervention in literacy depending on context, and mathematical reasoning proficiency measured 0% across the sample.
The study is a single peer-reviewed validation by the system's own development team, not yet an independent replication or a national rollout, and it does not address student data protection or governance safeguards for the assessment data it collects.
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