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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".