Sustainability in the Built Environment Reflected in Serious Games: A Systematic Narrative Literature Review
Bibliographic record
Abstract
The increasing complexity of the built environment—encompassing three-dimensional spatial dynamics, environmental footprints, and socio-cultural dimensions—necessitates innovative educational tools. Serious games have emerged as immersive platforms bridging theoretical knowledge and practical application in this domain. This narrative literature review examines the extent to which serious games effectively integrate and reflect sustainability principles within the context of the built environment, as well as their strategies for engaging learners. A comprehensive search was conducted across multiple databases using keywords such as “serious games,” “built environment,” and “sustainability.” The review identifies that while many games address tangible challenges like retrofitting simulations and resource management, their incorporation of sustainability concepts is often superficial. Critical aspects such as inclusivity, stakeholder engagement, and alignment with SDGs are frequently underrepresented. Furthermore, a lack of a common language among stakeholders and the tendency to focus on isolated aspects of sustainability, rather than adopting a holistic approach, were noted. Despite these limitations, the engaging nature of these games that are based on real scenarios offers potential for impactful learning experiences. However, challenges persist, including technical constraints, pedagogical limitations, and deeper epistemological and ethical tensions in game design. The findings underscore the need for a more integrated and comprehensive approach to embedding sustainability in serious games, along with more effective engagement strategies to ensure they function as impactful tools for education and learning in the built environment domain.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".