VR/AR Environment for Training Students on Engineering Applications and Concepts
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
Modern technologies in virtual reality (VR) and augmented reality (AR) provide unique features that can be used to facilitate tasks in everyday life. Several courses can be built using augmented reality, such as engine maintenance, computer maintenance, chemistry lab, etc. Augmented reality technologies provide dynamic and interactive instructions to resolve a problem or present required concepts. Building an educational system based on augmented reality is not an easy task due to some difficulties and challenges, such as the cost of augmented reality tools and other hardware and software required. Also, training students with engineering concepts and precise parts involves a lot of analysis and practice to know problems and then design solutions. The paper aims to develop a virtual educational environment for training students in engineering sectors in practical laboratory sessions based on AR/VR techniques. The proposed system provides a safe and low-cost environment to train the student different concepts in engineering sector, such as basic concepts in electrical, mechanical and renewable energy engineering.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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