Factors affecting ChatGPT use in education employing TAM: A Jordanian universities’ perspective
Bibliographic record
Abstract
The widespread adoption of artificial intelligence (AI) technologies, including ChatGPT, into education, has become a focal point of attention in recent years. This research explores the connections among perceived usefulness (PU), perceived ease of use (PEOU), attitude toward using ChatGPT (ATUC), and intention to use ChatGPT (ITUC) within Jordanian universities. A survey was employed to gather information from 523 university students in Jordan, and the hypotheses were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness and perceived ease of use positively impacted attitude toward using ChatGPT and intention to use ChatGPT. Attitude toward using ChatGPT positively impacted intention to use ChatGPT. Implications from this research are crucial to provide developers, instructors, and institutions in Jordan with useful information to help them successfully incorporate ChatGPT into the educational process.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".