Moirai: A No-Code Virtual Serious Game Authoring Platform
Bibliographic record
Abstract
Serious games, that is, games whose primary purpose is education and training, are gaining widespread popularity in higher education contexts and have been associated with increased learner memory retention, engagement, and motivation even among learners with special needs. Despite these benefits, serious games have fixed scenarios that cannot be easily modified, leading to predictable and dull experiences that can reduce user engagement. Therefore, there is a demand for tools that allow educators to create new modifications and customize serious game scenarios, and avoid the fixed-scenario problem and a one-size-fits-all approach. Here, we present and detail our novel virtual serious games authoring platform called Moirai, which uses a no-code approach to allow educators who may have limited (or no) prior programming experience to use a diagram-based interface to author and customize serious games focused on decision and communication skills development. We describe two case studies, each of which involved creating serious games for nursing education (one for mental health education and the other for internationally educated nurses). The usability of both games was qualitatively evaluated using the system usability scale (SUS) questionnaire and achieved above-average usability scores.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".