Exploring the Effects of Using AI Technologies in Higher Education Institutions in UAE
Bibliographic record
Abstract
This study aimed to explore the effects of using artificial intelligence (AI) technologies in higher education institutions in UAE on students and faculty members. Through designing a survey on Google Form and adopting a descriptive analytical approach, the intended goals were met. This survey employs the five point Likert scale and includes two main parts. The first part collects data about (gender, name of the university, and academic rank). The second part targets two areas. The first area is the effects of using AI technologies in higher education institutions in UAE on students and the second area is the effects of using AI technologies on faculty members. The researchers shared the survey link on several WhatsApp groups that target faculty members in eight universities in UAE. The survey was filled by 244 faculty members. Thus, the purposive sampling method was used. SPSS program was used for conducting an analysis for the collected data. It was found that using AI technologies in higher education institutions in UAE has positive effects on students and faculty members. In terms of students using AI technologies develops the critical thinking, research, critical thinking and time management skills of students.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".