Exploring Persuasive Games for Emotion Regulation: A State-of-the-Art Scoping Review
Bibliographic record
Abstract
Persuasive games, designed and tailored to change users’ behavior, have enhanced user experience across various domains. However, their role in emotion regulation remains understudied, despite the increasingly immersive environments they create that trigger emotional responses. Sensors have emerged as key artificial intelligence (AI) tools in persuasive games supporting emotion regulation compared to other AI technologies, such as chatbots. Yet the effectiveness, challenges, and applications of these sensors lack comprehensive exploration. Our scoping review analyzed 32 articles published from 2013 to 2023 on persuasive games using AI-based sensing technologies for emotion regulation. Results from 27 articles (84.8%) reporting the effectiveness of sensors revealed mostly large to moderate effects (d ≥ 0.8 ≤ 0.4). Common challenges with integrated sensors (camera systems, facial recognition, smartwatches, and altimetric pressure sensors) included emotion detection accuracy and ethical concerns. We provide design recommendations for developing ethically sound and effective persuasive games for emotion regulation in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".