PROMPT ENGINEERING COMPETENCE AND AI-ASSISTED ACADEMIC WRITING QUALITY AMONG ESL LEARNERS

This study examined prompt-engineering competence and AI-assisted academic writing quality among 81 ESL learners at Cagayan State University–Aparri in SY 2025–2026 using a descriptive-correlational design. It assessed prompt skills—clarity, specificity and precision, contextual/guiding information, and sequencing/iteration—and evaluated AI-generated texts for content, organization/coherence, grammar and language accuracy, vocabulary, task relevance, and citation/structure. Data came from a validated instrument and performance-based assessment. Results showed learners were generally competent in prompt engineering and produced high-quality AI-assisted academic texts. Most profile variables did not significantly affect outcomes, though academic strand, monthly allowance, general weighted average, and type of AI tools used showed significant differences. All prompt-engineering components were highly and significantly correlated with all dimensions of AI-assisted writing quality. The study concludes prompt-engineering competence critically enhances AI-assisted academic writing and recommends a structured instructional guide to improve learners’ prompt construction and output quality. Implementation should include practice exercises and assessment rubrics for sustained improvement.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-28
DOI
https://doi.org/10.5281/zenodo.19842239
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
0.00
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article

PROMPT ENGINEERING COMPETENCE AND AI-ASSISTED ACADEMIC WRITING QUALITY AMONG ESL LEARNERS

MARK JOSEPH V. TINTERO
Zenodo (CERN European Organization for Nuclear Research)
E-Learning and COVID-19
article

PROMPT ENGINEERING COMPETENCE AND AI-ASSISTED ACADEMIC WRITING QUALITY AMONG ESL LEARNERS

MARK JOSEPH V. TINTERO
article en

Abstract

This study examined prompt-engineering competence and AI-assisted academic writing quality among 81 ESL learners at Cagayan State University–Aparri in SY 2025–2026 using a descriptive-correlational design. It assessed prompt skills—clarity, specificity and precision, contextual/guiding information, and sequencing/iteration—and evaluated AI-generated texts for content, organization/coherence, grammar and language accuracy, vocabulary, task relevance, and citation/structure. Data came from a validated instrument and performance-based assessment. Results showed learners were generally competent in prompt engineering and produced high-quality AI-assisted academic texts. Most profile variables did not significantly affect outcomes, though academic strand, monthly allowance, general weighted average, and type of AI tools used showed significant differences. All prompt-engineering components were highly and significantly correlated with all dimensions of AI-assisted writing quality. The study concludes prompt-engineering competence critically enhances AI-assisted academic writing and recommends a structured instructional guide to improve learners’ prompt construction and output quality. Implementation should include practice exercises and assessment rubrics for sustained improvement.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 3%
E-Learning and COVID-19
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