Exploring the Benefits and Challenges of Digital Game-Based Learning for K-12 Students: A Narrative Review of the Literature
Bibliographic record
Abstract
Many K-12 students struggle to stay motivated and engaged in traditional K-12 classrooms. Digital game-based learning (DGBL) has emerged as a promising pedagogical strategy to increase student engagement, motivation, and academic performance. Numerous literature reviews have explored the use of DGBL; however, many are dated, narrowly focused on specific subject areas or outcomes, or include mixed-age populations, limiting their relevance for current K–12 contexts. This narrative review explored the benefits and challenges of digital game-based learning (DGBL) in K–12 education by analyzing 22 peer-reviewed studies published between 2010 and 2025. Drawing on a scoping review methodology and inductive thematic analysis, the review identified five key benefits of DGBL: enhanced student attitudes toward learning, increased extrinsic and intrinsic motivation, improved self-efficacy, and greater domain knowledge acquisition. Two key challenges emerged: the negative effects of competitive game mechanisms (e.g., leaderboards) and inconsistent student acceptance of DGBL as a viable educational approach. The review emphasizes the need for thoughtful pedagogical design that balances motivational elements with inclusive, supportive practices. Educational implications include aligning games with curricular goals, scaffolding student learning, and promoting digital game literacy. Future research should focus on effective pedagogical strategies, teacher support, curriculum integration, inclusive practices, and the development of robust evaluation tools to evaluate DGBL’s impact in K–12 contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".