A Multidisciplinary Approach to Enhancing Infantry Training through Immersive Technologies
Bibliographic record
Abstract
There is a growing interest in the Canadian Army for using off-the-shelf computer games in training because of the interactivity and engagement they create for the trainees. Unfortunately, existing solutions do not always satisfy the aggregated training needs of infantry. In this paper, we briefly describe the challenges and shortcomings of both physical and virtual training systems, and the specific training needs identified through field studies in partnership with the Department of National Defence Canada. We then describe research solutions addressing these specific challenges, such as novel interaction methods in immersive environments, usable speech recognition, simulated weapons (laser rifles, electronic flashbang, etc.), flexible serious gaming platforms with intelligent agents and cognitive models, and mobile control interface for instructors. We also present the outcomes of field observations revealing what additions are required to improve learning scenarios for serious games. Within our research project we created a platform for developing and validating novel interaction methods, technologies, and devices that create mixed-reality immersive training systems that are safer, more cost efficient, and more effective for learning than physical environments or purely virtual reality systems. The immediate use of our mixed-reality system is in practicing engagement skills and training personnel in the rapid application of judgment and of rules of engagement and in the use of force. We conclude by describing the findings of human-subject evaluations conducted with an implementation of our research platform, presenting lessons from technology-specific feasibility, to educational/learning impact, and to human factors of interacting with an intelligent, immersive, training system. These findings provide encouraging evidence that such solutions will allow infantry, law enforcement, or public safety personnel to train using a virtual environment in a manner similar to training in a physical environment.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".