Protocole expérimental visant l'étude de l’IA centrée sur l'humain dans le contexte de l'Industrie 5.0: Application en réalité augmentée
Bibliographic record
Abstract
Industry 4.0's primary goal is generally to create a learning and agile enterprise, capable of adapting continuously to changing conditions through new technologies' utilization. However, the results of previous developments remain mitigated, mainly due to a primarily techno-centric approach. In contrast, the concept of Industry 5.0 is now defined as a human-centred approach, including social, societal, and environmental considerations. The evolution towards new models of agile organizations implies, in particular, greater autonomy for teams based on improved and accelerated decision-making. However, I4.0 technology's influence on the performance, motivation, engagement, and cognitive load of employees in a production setting remains largely under-researched. In this article, we present an experimental methodology to address this gap. We discuss its future application to a use case in which artificial intelligence (AI) and augmented reality (AR) are implemented to aid the operator in an error-detection manufacturing task. Finally, the methodological choices are elucidated, in preparation for the upcoming testing and operational implementation phases of the system. Results from the application of the experimental methodology will be used to identify the key factors contributing to the success and failure of AI and AR system design and implementation. Ultimately, we aim to understand how to promote positive outcomes for the employees using the system, in terms of performance, engagement, motivation, and autonomy.
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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 | 0.063 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".