Immersive Environment with Virtual Reality and Eyetracking in the Public Sector: Modernization of the TCE-GO Server onboarding and Training Process
Bibliographic record
Abstract
The combination of virtual reality (VR) and biofeedback technologies, such as eye tracking, offers new possibilities for measuring user presence and attentional focus in immersive environments. In the context of institutional training, this synergy proves particularly promising. This study presents a proposed VR-based onboarding and training platform for new employees of the Court of Accounts of the State of Goiás (TCE-GO), with a focus on visual attention measurement. By employing 3D modeling, integration with eye tracking technology, and data analysis through AI models, the project aims not only to familiarize new employees with the court’s workflows but also to train technical skills and objectively assess their levels of engagement. The research is grounded in the Design Science Research (DSR) methodology, encompassing the design and development of the onboarding environment. Expected outcomes include increased efficiency and scalability in the training process, as well as a pioneering application of VR and biofeedback in employee training for Courts of Accounts.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".