Unveiling Indonesian Higher Education Students’ English Academic Writing Misconduct in The Era of Technology & AI: Comprehension vs Practice
Bibliographic record
Abstract
The widespread use of internet-based media and AI-powered tools for learning has provided students with convenient access to information and writing assistance. However, rather than improving their English academic writing skills, such reliance may lead to academic misconduct, including plagiarism and unethical use of AI-generated content. Such concerns motivate the researchers to gain insights from 50 second-semester students in Indonesia enrolled in two English essay writing courses regarding academic integrity and violations. Employing a qualitative descriptive approach, data were collected through a questionnaire assessing students’ understanding of academic integrity, followed by an analysis of 94 essays from 47 students before and after completing the questionnaire. Findings revealed a notable gap between students’ conceptual understanding of academic integrity and their actual writing practices. Although students recognized the importance of ethical writing, instances of plagiarism from internet sources and AI-generated text incorporation persisted. These discrepancies stem from underdeveloped English writing proficiency, lack of awareness about plagiarism, and dependency on digital assistance. The study underscores the need for early and continuous instruction in academic integrity and English writing skills, along with institutional policies and support systems to mitigate academic misconduct in English academic writing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".