Identifying Key Principles and Commonalities in Digital Serious Game Design Frameworks: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital serious games (DSGs), designed for purposes beyond entertainment and consumed via electronic devices, have garnered attention for their potential to enhance learning and promote behavior change. Their effectiveness depends on the quality of their design. Frameworks for DSG design can guide the creation of engaging games tailored to objectives such as education, health, and social impact. OBJECTIVE: This study aims to review, analyze, and synthesize the literature on digital entertainment game design frameworks and DSG design frameworks (DSGDFWs). The focus is on conceptual frameworks offering high-level guidance for the game creation process rather than component-specific tools. We explore how these frameworks can be applied to create impactful serious games in fields such as health care and education. Key goals include identifying design principles, commonalities, dependencies, gaps, and opportunities in the literature. Suggestions for future research include empathic design thinking, artificial intelligence integration, and iterative improvements. The findings culminate in a synthesized 4-phase design process, offering generic guidelines for designers and developers to create effective serious games that benefit society. METHODS: A 2-phase methodology was used: a scoping literature review and cluster analysis. A targeted search across 7 databases (ACM, Scopus, Springer, IEEE, Elsevier, JMIR Publications, and SAGE) was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Studies included academic or industry papers evaluating digital game design frameworks. Cluster analysis was applied to categorize the data, revealing trends and correlations among frameworks. RESULTS: Of 987 papers initially identified, 25 (2.5%) met the inclusion criteria, with an additional 22 identified through snowballing, resulting in 47 papers. These papers presented 47 frameworks, including 16 (34%) digital entertainment game design frameworks and 31 (66%) DSGDFWs. Thematic analysis grouped frameworks into categories, identifying patterns and relationships between design elements. Commonalities, dependencies, and gaps were analyzed, highlighting opportunities for empathic design thinking and artificial intelligence applications. Key considerations in DSG design were identified and presented in a 4-phase design baseline with the outcome of a list of design guidelines that might, according to the literature, be applied to an end-to-end process of designing and building future innovative solutions. CONCLUSIONS: The main benefits of using DSGDFWs seem to be related to enhancing the effectiveness of serious games in achieving their intended objectives, such as learning, behavior change, and social impact. Limitations primarily seem to be related to constraints associated with the specific contexts in which the serious games are developed and used. Approaches in the future should be aimed at refining and adapting existing frameworks to different contexts and purposes, as well as exploring new frameworks that incorporate emerging technologies and design principles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".