You are entitled to access the full text of this documentAntecedents of intention to use project management among educational institution: Empirical study in Jordan
Bibliographic record
Abstract
The present study examined the perception of users towards the role of artificial intelligence (AI) in improving personal learning profile (PLP), personal learning network (PLN) and personal learning environment (PLE). Additionally, the impact of PLP, PLN and PLE on perceived ease of use, perceived effectiveness and perceived usefulness in improving the general attitude and satisfaction of users in their intention to use project management was examined. Results showed the impact of PLE on perceived ease of use and perceived usefulness, significant impact of PLP on perceived effectiveness, and impact of student pressure on intention to use project management. Data were obtained from professionals and students with experience in the use of project management modules. Notably, the obtained data were fully based on the perceptions of the respondents, resulting in potential self-perception bias. Perceptions of users towards PLP, PLN and PLE were integrated into the technology acceptance model framework of this study, to understand their impact on the general attitude and satisfaction of learners. Using AI can enhance learner attitude and satisfaction while creating more engaging e-learning, proving the crucial role of AI in forming the right environment through learner profile match.
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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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".