Assessing the Training Effectiveness of an Intelligent Tutoring System for Marksmanship Skills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of embedded trainers and intelligent tutoring systems (ITS) may allow modern militaries to maintain high proficiency in marksmanship skills despite limitations on training personnel and resources. A system embedding an ITS in a future rifle concept was developed to examine the potential of such technologies. This system was based on two main concepts: 1) instance-based learning and practice that uses a Smart Sight System to present a perceptual cue adjusted for the target’s properties and environmental variables and 2) rule-based learning that relies on artificial intelligence algorithm that delivers training tips and feedback to participants. The experimental task involved engaging moving targets on a virtual shooting range. Three types of training were compared: a control group that received no additional training, a standard Canadian Armed Forces (CAF) training group, and an advanced training group that received ITS training. The results showed that additional training—both CAF and ITS—improved shooting accuracy. The performance in the ITS condition was consistently better than in the Control group or in the CAF training group on selected measures of performance.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it