The Transformative Potential of Role-Playing Games—: From Play Skills to Human Skills
Bibliographic record
Abstract
Background. Several authors from different fields have already mentioned the educational potential of role-playing games (RPG). As tabletop role-playing games (TRPG) present some similarities with small adult groups in learning and personal development situations, what about their transformative potential? Aim. The purpose of this article is to describe the tabletop role-playing game’s emerging context, a few of its specificities and functions, to show links with several education, play and game, and personal development theories, and to raise awareness about its transformative potential. Methods. Three complementary approaches were used: a literature review (academics and role-players), action-researches through a transformative role-playing game (“TF-RPG” – a TRPG plus a debriefing), and data cross-analysis. Results. Participants are involved in the TF-RPG through four levels of reality, namely the character, player, person, and human being, which can be associated with four dimensions of learning: knowing, doing, being, and relating. The unveiling of links between the TF-RPG experience and their personal journey offers the participants various ways of learning and paths towards personal development. Conclusion. TRPGs are particularly effective to foster knowledge acquisition, develop role-play skills, strengthen team building, encourage collaborative creativity, and explore one’s personal development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".