Perceptions of Artificial Intelligence among Philippine Undergraduate Students: Examining Instrument Construct and Demographic Influences on Knowledge and Beliefs
Bibliographic record
Abstract
Research on Artificial Intelligence (AI) is ubiquitous, yet the perceptions of undergraduate students (UGS) in the Philippines regarding AI remain underexplored. We surveyed 537 UGS to evaluate their knowledge and beliefs about AI, aiming to inform policy guidelines in higher education institutions (HEIs). Prior to the survey, an exploratory factor analysis was conducted to establish the construct validity of an adapted instrument, revealing three factors: (1) perceived threat to human autonomy and employment, (2) perceived academic and economic utility, and (3) perceived negative consequences. Findings indicated that UGS generally possess a moderate level of self-reported knowledge about AI. Their beliefs were varied, showing a tendency towards neutrality regarding factor 1, agreement on factor 2, and apprehension towards factor 3. Demographic factors did not significantly influence these beliefs. However, gender, age group, program of study, and year level significantly affected their knowledge. The UGS also recognized the benefits of using AI, including learning assistance and interactive capabilities. Diminished interpersonal relationships and inaccurate information are among the drawbacks. Based on these findings, we urge policymakers in Philippine HIEs to develop informed guidelines on AI integration that address the identified concerns while leveraging its perceived benefits for ethical and responsible use of AI.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".