Board 24: Development of Multi-User-enabled, Interactive, and Responsive Virtual/Augmented Reality-based Laboratory Training System
Bibliographic record
Abstract
The Unit Operations Laboratory (UOL) is a place where third-year chemical engineering students can apply their engineering and science concepts on pilot-scale equipment.However, the physical lab is resource-intensive, requiring protective equipment and constant supervision.In addition, due to limited units for large groups of students, students perform experiments according to the rolling program schedule, making alignment with lecture teaching and hands-on learning challenges.The research team focuses on increasing the accessibility of the UOL by using simulation gaming in standard, virtual reality and augmented reality modalities as an educational tool.The "Virtual Unit Ops Lab" application places students in an immersive simulated environment of the physical lab, where they can get practical experiences without the difficulties of an in-person lab by using specialized headsets and controllers, which allows the student to move and interact with various parts of the machine physically.Developed with Unity software, the application serves as a digital twin to an existing lab, which allows for an immersive simulation of the full-scale lab equipment, in addition to enhanced learning features such as the ability to display the current action performed by the user and to provide visual/audio feedback for correct and incorrect actions.The application also supports the use by multiple "players" (i.e., it has the "multiplayer" option), where multiple students can communicate and discuss their current step.As a work in progress, a non-player-character chatbot (generative AI responses) is being developed for existing applications using OpenAI's GPT-3.5, which provides designated information to a student in a conversational manner.Additionally, a supplemental "Augmented Unit Ops Lab" application uses Augmented Reality, which superimposes three-dimensional flow diagrams onto the Heat Exchanger through the view of a phone camera during the in-person labs.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".