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 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".