The use of serious games for psychological education and training: a systematic review
Bibliographic record
Abstract
Introduction The present systematic review aims to synthesize and critically analyze the use of serious games in the professional training and education of psychologists and psychology students. Methods Following PRISMA guidelines, database searches from inception to July 2023 (PsycINFO, PubMed, Web of Science, and Scopus) yielded 4,409 records, of which 14 met the eligibility criteria, including 17 studies. Quality assessment was performed using the Newcastle-Ottawa Scale and the Risk of Bias Tool for Randomized Trials. Results The review identified three pivotal areas where serious games demonstrated significant educational impact: enhancing psychological traits and attitudes (e.g., prejudice, empathy), promoting theoretical knowledge acquisition (e.g., biopsychology), and developing professional skills (e.g., investigative interview with children). Serious games, particularly those providing feedback and modeling, significantly enhance the quality of learning and training for psychology students and professionals. Discussion Key findings revealed that serious games operate by offering realistic, engaging, and flexible learning environments while mitigating risks associated with real-world practice. Methodological limitations, including moderate to high risk of bias in many studies, especially those that relied on cross-sectional data, underscore the need for rigorous designs and long-term evaluations. Practical implications suggest integrating serious games into curricula to address gaps in experiential learning for psychologists, facilitating skill development and knowledge retention. Future research should explore the long-term impact of serious games on professional competencies and assess their applicability across diverse educational contexts.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".