Evaluating Grammarly-Assisted Post-Editing of Google Translate Outputs in Indonesian-English Academic Texts: An Exploratory Corpus Study with Expert Assessment

Ismiyati Hasiru (Universitas Negeri Gorontalo)
Novriyanto Napu (Universitas Negeri Gorontalo)
Suleman S. Bouti (Universitas Negeri Gorontalo)

Script Journal: Journal of Linguistic and English Teaching
Vol. 11 No. 2 (2026): August

Background: This mixed-method study investigates the effectiveness of integrating Google Translate (GT) and Grammarly as a combined English-for-Academic-Purposes (EAP) learning and teaching intervention, aiming to improve the accuracy, clarity, and readability of Indonesian-to-English academic texts. Methodology: The study employs a sequential explanatory design combining quantitative error analysis with qualitative expert feedback. Purposive sampling was used to select a 7,500-word corpus of published Indonesian academic articles across six disciplines: linguistics, technology, economics, engineering, medical science, and law. Six EAP instructors/translators with advanced IELTS scores served as raters. Machine-generated translations via GT were post-edited using Grammarly. Quantitative data consisted of 519 Grammarly-detected writing issues categorized into clarity, correctness, delivery, and engagement. Qualitative data were collected through open-ended questionnaires based on Machali’s Translation Quality Assessment rubric. Descriptive statistics and thematic coding were used for analysis. Findings: Grammarly post-editing reduced grammatical and stylistic errors by over 54% in correctness and 35% in clarity categories. However, human intervention remained essential for addressing semantic nuance and cultural appropriateness. Raters evaluated engineering and technology texts more favorably (mean score = 5.8/7) than linguistics texts (mean = 4.3/7), indicating domain-specific variation in translation quality. Conclusion: The integration of GT and Grammarly improves the overall quality of machine-translated academic texts, particularly in grammatical accuracy and clarity. Nevertheless, expert human involvement is still required to ensure semantic precision and contextual appropriateness for publication-ready outputs. Originality: This study introduces a practical way to combine Google Translate and Grammarly for EAP learning, while showing that human expertise is still essential for high-quality academic writing.