Adapting Content Generation Knowledge from Diverse Fields to Construction: A Systematic Review on Content Generation in Extended Reality Based Training
Bibliographic record
Abstract
Extended Reality (XR) based training applications designed for the construction environment is rapidly gaining popularity, but creating high-quality content remains a significant challenge.Traditional content generation methods, such as manual 3D modeling, paper-based checklist creation, the creation of static training simulations, reliance on pre-recorded video and 2D diagrams for instruction, and manual annotation of 3D models for training purposes, are timeconsuming, require specialized skills, and are often cost-prohibitive, limiting scalability and accessibility.While other industries have successfully implemented automation and artificial intelligence (AI) for content creation, the construction field continues to rely heavily on manual processes.By learning from advancements and best practices in other fields, the construction industry can overcome these challenges and unlock the full potential of XR for training its workforce.This systematic review analyzes and synthesizes the current literature on content generation techniques for XR-based training.It examines different content generation methods, identifies some benefits and challenges, and explores their potential suitability for various training domains and objectives.The review explores content generation methods, XR technology platforms, interaction types, and learning environments, with a focus on their potential to inform best practices for construction.Following PRISMA guidelines, the review included studies from top-quantile peer-reviewed journals and conferences over the past 20 years, identifying 97 direct relevant studies from Scopus, ACM library, and Web of Science.The review indicates that while 3D modeling remains dominant in XR training content creation, AI-powered generation is still emerging with limited applications specifically for training content.Initial findings suggest potential benefits such as increased learner engagement and reduced development time, but challenges in accuracy and ethical considerations persist.The insights gained can inform the development of guidelines and best practices for creating high-quality XR training content tailored to the construction environment.
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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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".