Effect of experiential learning based AI‑generated aging video simulation on knowledge, attitude and gerontophobia in nursing students
Bibliographic record
Abstract
BACKGROUND: With global ageing accelerating, nursing students’ knowledge and attitudes toward older adults’ influence care quality. We evaluated an experiential learning–based, AI-generated ageing video simulation aligned with Kolb’s cycle. DESIGN: One-group pretest–posttest quasi-experimental design with undergraduate nursing students at RAK College of Nursing (UAE). Gerontophobia paper Vancouver. METHODS: Students completed baseline measures, viewed an AI-generated ageing simulation embedded in Kolb’s stages (concrete experience; reflective observation/abstract conceptualization; active experimentation), and repeated measures immediately post-intervention. Outcomes: knowledge (Palmore Facts on Aging Quiz), attitudes (Kogan’s Attitudes Toward Old People), and gerontophobia (Anxiety about Aging Scale). Paired tests assessed pre/post differences; effect sizes (Cohen’s d_av; Hedges’ g) summarized magnitude. RESULTS: N = 107 (66.4% female, mostly 20–29 years). Knowledge increased 13.00 ± 3.00 → 22.18 ± 2.65 (p = 0.001; d = 3.25; g = 3.23). Attitudes improved 26.16 ± 6.38 → 33.17 ± 5.92 (p = 0.0002; d = 1.14; g = 1.13). Ageing-anxiety decreased 59.5 ± 16.2 → 50.3 ± 14.2 (p < 0.001; d = − 0.61; g = − 0.60). Directional sign indicates reduced anxiety. CONCLUSIONS: A brief, AI-generated simulation grounded in experiential learning substantially improved knowledge, attitudes, and reduced gerontophobia. Integrating such media into undergraduate curricula may strengthen gerontological competencies; longer follow-up and controlled comparisons are warranted.
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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 | 0.001 | 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 | 0.000 |
| 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".