Would Lecturers Use AI-Based Software to Write Scientific Article? A Quantitative Approach in Indonesia
Bibliographic record
Abstract
AI-based (artificial intelligence) software utilization is increasingly widespread, including in education.On the one hand, AI-based software offers advantages for producing quality writing, such as converting voice into text, summarizing paragraphs, and improving grammar.There are pros and cons to each argument.Interestingly, permission or prohibition on using software is only for students, not lecturers.Moreover, lecturers should write quality scientific articles productively so that AI-based software can facilitate writing.Therefore, this research explores the driving and inhibiting factors that are thought to influence the use of AI-based software to assist lecturers in writing scientific articles.Following its goals, this research identified the potential factors through six hypotheses and tested them using PLS-SEM.This research recruited 110 lecturers in Indonesia as respondents to express their quantitative perceptions.It found that Product Quality (QLT) and Security (SEC) factors influence lecturers' use of AI-based software in scientific writing.However, four other variables (Motivation/MOT, Supporting Ability/SUP, Subjective Norms/NOR, and Individual Ability/ABL) had no influence.The finding exposed software quality has a vital role in engaging lecturers' intention to use AI-based software considering its characteristics to satisfy their needs: usefulness, easiness, accuracy, and efficiency.Also, lecturers are concerned with information security since AI-based software captures personal data, including user behavior during its usage.This research promotes practical implications for universities to regulate AI-based software, considering its benefits have been recognized by lecturers.However, its misuse can lead to the university's credibility in research and should be mitigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".