Antecedents of the adoption, use, and legal risks of ChatGPT in Jordan
Bibliographic record
Abstract
The current study aims to assess the factors that could affect students’ use of ChatGPT. The study proposed a theoretical model that included six factors. Data was collected from 518 students using a questionnaire. The data were analyzed using Structural Equation Modeling to identify the relationships and test the hypothesis. The findings revealed that performance expectancy and social trust significantly influenced students’ intentions to use ChatGPT. Contrary to expectations, both social influence and effort expectancy had insignificant effects. By elucidating the core factors affecting the utilization of ChatGPT, trust had a significant impact on the intention to use ChatGPT. Furthermore, trust mediates the relationship between perceived security and intention to use ChatGPT, this study can provide valuable insights for policymakers. Moreover, this study contributes to the existing literature by setting the foundation for future research seeking a deeper understanding of the factors influencing the use of other AI technologies in teaching and learning in Electronic Public Facilities.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".