The Effect of Using Artificial Intelligence on Learning Performance in Iraq: The Dual Factor Theory Perspective
Bibliographic record
Abstract
The adoption of artificial intelligence applications in higher education plays an important role in the improvement of the quality of education and learning practices and overcomes many educational' issues. The purpose of this study is to examine the factors influencing learning performance by using artificial intelligence in the educational process. The research model has been developed based on dual factor concept through examining “enablers” and “inhibitors” factors of artificial intelligence adoption in higher education toward improving the Learning Performance. The research model has been built based on a combination of the following theories, constructivism, TAM3, UTAUT, BM, status quo bias theory. The hypothesized model is validated empirically via a questionnaire including 57-item based on 5-point Likert scales completed by 383 respondents (random sampled). Structural equation modeling was used to evaluate the proposed model by analyzing the confirmatory factor and path effects across the AMOS software. The results demonstrate that the indicators of model fitness showed good fit. As for the results of the hypothesis test, it is clear that the results of the analysis show that the interaction and the engagement of learning have a significant effect on the collaboration for learning and thus have a significant effect on the learning performance. Perceived enjoyment, perceived usefulness, and perceived ease of use have a significant effect on using artificial intelligence. Facilitating conditions have no significant impact on using artificial intelligence. Consciousness has a positive effect on use while perceived risks and resistance do not significantly affect use and learning performance. The use of artificial intelligence has a positive and significant effect on learning performance.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".