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Indonesian Journal of English Language Teaching and Applied Linguistics

ijeltal
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Published by Institut Agama Islam Negeri Samarinda

ISSN : 25276492 EISSN : 25278746 DOI : -

Core Subject : Education,

Languange, Linguistic, Communication & Media

Indonesian Journal of English Language Teaching and Applied Linguistics, e-ISSN: 2527-8746; p-ISSN: 2527-6492, published by English Department, Teacher Training and Education Faculty, State Islamic Institute of Samarinda (IAIN Samarinda). It is a peer-reviewed journal of Language and Linguistics, including: Applied Linguistics and English Language Teaching. The journal is published twice a year; May and November

Arjuna Subject : -

Articles 232 Documents

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Indonesian EFL Students’ Thesis Writing in the Age of AI: Practices and Ethical Perspectives Mali, Yustinus Calvin Gai
IJELTAL (Indonesian Journal of English Language Teaching and Applied Linguistics) Vol 11, No 1 (2026): Indonesian Journal of English Language Teaching and Applied Linguistics
Publisher : Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/ijeltal.v11i1.2439

This study aims to explore how Indonesian EFL university students use Artificial Intelligence (AI) tools to support their undergraduate thesis writing, why they use those tools, and the AI-use policies they expect to have in their thesis writing process. To achieve these goals, the researcher conducted a qualitative case study involving five female EFL undergraduate students who were in the process of completing their theses at an English Education Program at a private university in Indonesia. Research data were collected through an in-depth semi-structured interview, supported by the students’ thesis draft to help them recall their thesis writing experiences. The researcher considered four rigor standards for qualitative studies: credibility, transferability, dependability, and confirmability to ensure the quality of the research findings. The inductive data analysis results revealed that students used four AI tools; ChatGPT, DeepL, Grammarly, and Quillbot in their thesis writing process to help them paraphrase, generate writing ideas, translate words and sentences, and select appropriate vocabulary. These tools were used because the students believed they might help enhance the quality of their writing, write faster, and increase their confidence in writing their thesis. At the same time, the students were also aware of the risks of being too dependent on AI tools and the inaccuracy of AI-generated texts and suggestions, which encouraged them to verify the AI outputs before incorporating them into their thesis. Practical implications for EFL lecturers who are supervising thesis students and directions for future research were presented.

Gemini API-Based Automated English Paragraph Scoring Aligned with High School Thai Curriculum Writing Indicators Deeboonmee Na Chumphae, Phisit
IJELTAL (Indonesian Journal of English Language Teaching and Applied Linguistics) Vol 11, No 1 (2026): Indonesian Journal of English Language Teaching and Applied Linguistics
Publisher : Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/ijeltal.v11i1.2553

This research aims to develop and evaluate an automated English paragraph scoring system using the Gemini API aligned with the Thai upper secondary curriculum's writing indicators, to address issues related to teacher workload and delayed feedback in writing assessments. The system integrates the Gemini 2.5 Pro API with a prompt-engineering framework designed to simulate expert EFL assessors. This research employs a sequential mixed-methods research approach. For the quantitative component, 160 upper secondary EFL students in Thailand were sampled from their written assignments, consisting of three expository paragraph assignments aligned with the Thai core curriculum. Cluster sampling was used to select participants. The students' writings were assessed using a validated analytical evaluation criterion comprising four aspects. The essays were independently scored by three evaluators, and the results were compared to automated scores generated by a Gemini-based system. Reliability between human evaluators was first checked using the Intraclass Correlation Coefficient (ICC), and the agreement between human and AI scores was measured using the Quadratic Weighted Kappa (QWK). The results showed a high level of agreement between the Gemini-generated scores and the human evaluators (QWK = 0.82), indicating that the system can approximate human judgment in evaluating English as a Foreign Language writing. Qualitative analysis of the AI-generated feedback further revealed that the system could provide diagnostic recommendations related to grammar, vocabulary, and sentence structure. These findings suggest that the system can support teachers in reducing grading workload while providing timely, criteria-based feedback to enhance students’ writing development.

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Umar Fauzan

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ijeltalj@gmail.com

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Abstract

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Home Page

OAI Link

Editorial Team

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Google Scholar

Contact Name Umar Fauzan

Contact Email dr.umarfauzan@gmail.com

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Journal Mail Official ijeltalj@gmail.com

Editorial Address -

Location Kota samarinda, Kalimantan timur INDONESIA

Abstract

Abstract

Contact Name
Umar Fauzan

Contact Email
dr.umarfauzan@gmail.com

Phone
-

Journal Mail Official
ijeltalj@gmail.com

Editorial Address
-

Location
Kota samarinda,

Kalimantan timur

INDONESIA