Moving-Target Intelligent Tutoring System for Marksmanship Training
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
Abstract Intelligent tutoring systems (ITSs) may augment military training systems and mitigate existing limitations in training personnel and resources. A study was conducted to investigate the effectiveness of an embedded rifle marksmanship ITS for Moving Targets (MT-ITS). MT-ITS has two main components: (1) a Smart Sight System that provides a perceptual cue to help trainees adjust their point of aim to account for a target’s speed, direction of movement, and distance, and (2) a performance-based algorithm that delivers shooting performance feedback to trainees. The MT-ITS was tested in an experiment where participants engaged moving targets in a virtual shooting range. Moving targets were presented at different speeds, direction of movement, and distances. Two types of marksmanship training were compared: with ITS and without ITS (a standard training). The ITS training group produced better hit rate and aiming accuracy scores than the standard training group, requiring less practice to achieve asymptotic results. Implications for the design of embedded trainers with ITS for marksmanship specifically and for training motor skills in general are discussed in the context of future research directions.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it