APPLICATION OF AI IN GRADING NEW QUESTION TYPES IN THE 2025 VIETNAMESE HIGH SCHOOL GRADUATION EXAM

The 2025 Vietnamese National High School Graduation Exam introduces a new three-format structure (multiple choice, True/False, short answer), while still relying on traditional OMR answer sheets that often lead to mechanical errors. This study proposes an AI-assisted grading system combining a direct-answer sheet format with Mathpix OCR handwriting recognition to address these limitations. Using a five-stage simplified Borg & Gall model—regulatory analysis, system development, expert validation, dual-phase trials, and pilot testing on 93 student papers—the system achieved 98.5% character recognition accuracy and high user satisfaction (Likert ≥ 4.0/5) in reducing errors and supporting all question types. Findings show that AI-enhanced direct-answer formats maintain OMR-level reliability while improving test validity. A proposed implementation roadmap includes expanding handwriting datasets, standardizing 300 dpi devices, province-wide teacher training, and integration into the official grading process.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0409/a.php" alt="Hit counter" /></p>

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Publication Details

Journal
European Journal of Education Studies
Published
2025-05-06
DOI
https://doi.org/10.46827/ejes.v12i4.6007
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
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article

APPLICATION OF AI IN GRADING NEW QUESTION TYPES IN THE 2025 VIETNAMESE HIGH SCHOOL GRADUATION EXAM

Tran Thi Thu, Quang‐Hieu Tran, Nguyễn Thị Thu Phương
European Journal of Education Studies
Educational Technology and Assessment
article

APPLICATION OF AI IN GRADING NEW QUESTION TYPES IN THE 2025 VIETNAMESE HIGH SCHOOL GRADUATION EXAM

Tran Thi Thu, Quang‐Hieu Tran, Nguyễn Thị Thu Phương
article en

Abstract

The 2025 Vietnamese National High School Graduation Exam introduces a new three-format structure (multiple choice, True/False, short answer), while still relying on traditional OMR answer sheets that often lead to mechanical errors. This study proposes an AI-assisted grading system combining a direct-answer sheet format with Mathpix OCR handwriting recognition to address these limitations. Using a five-stage simplified Borg & Gall model—regulatory analysis, system development, expert validation, dual-phase trials, and pilot testing on 93 student papers—the system achieved 98.5% character recognition accuracy and high user satisfaction (Likert ≥ 4.0/5) in reducing errors and supporting all question types. Findings show that AI-enhanced direct-answer formats maintain OMR-level reliability while improving test validity. A proposed implementation roadmap includes expanding handwriting datasets, standardizing 300 dpi devices, province-wide teacher training, and integration into the official grading process.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0409/a.php" alt="Hit counter" /></p>

European Journal of Education StudiesVol. 12(4)
Openalex Percentile: Top 83%
Educational Technology and Assessment
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