Amplifying Player Experience to Facilitate Prosocial Outcomes in a Narrative-Based Serious Game
Bibliographic record
Abstract
The rise and development of serious games have shown promise in addressing critical social issues, including school bullying. However, prior work often compares game-based interventions with the conventional non-game approach, failing to generate insights about which game features should be emphasized to create more effective games. To bridge this research gap, in light of video games’ advantages for creating immersive experiences that benefit persuasion, we created a narrative-based serious game addressing school bullying and conducted two studies (Study 1, N = 130; Study 2, N = 250) to explore the persuasive effects of two game features, respectively player–avatar similarity and in-game control, on player experience (including player–avatar identification, narrative engagement, and empathy) and prosocial intention. We found mixed results subject to player perspective such that only when players took the bully’s perspective did one of the game features—in-game control—successfully create the intended empathy via amplified narrative engagement toward the desirable prosocial intention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".