Bibliographic record
Abstract
Modern tabletop role-playing games (TTRPGs) have received increasing attention in library and educational contexts due to their ability to serve as an alternative experiential learning opportunity. However, common TTRPG safety frameworks lack consideration for the adoption and impact of AI tools. To address this gap, this study employs a qualitative approach to explore how AI tools are used in TTRPGs and players’ concerns related to fairness, bias, and safety. The findings aim to aid the development of more comprehensive safety frameworks for AI-integrated TTRPG experiences. Aventuriers et algorithmes: L'IA et l'évolution de la jouabilité des jeux de rôle sur table RésuméLes jeux de rôle modernes sur table (JDR) ont fait l'objet d'une attention croissante dans les bibliothèques et les contextes éducatifs en raison de leur capacité à fournir un apprentissage expérientiel alternatif. Cependant, les cadres de sécurité communs des JDR ne tiennent pas compte de l'adoption et de l'impact des outils d'IA. Pour combler cette lacune, cette étude utilise une approche qualitative pour explorer la manière dont les outils d'IA sont utilisés dans les JDR et les préoccupations des joueurs en matière d'équité, d’impartialité et de sécurité. Les résultats visent à faciliter l'élaboration de cadres de sécurité plus complets avec l’intégration de l'IA dans les JDR. Mots-clésJeux de rôle sur table; intelligence artificielle; éthique; IA responsable; JDR
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".