Emerging Telepresence Technologies for Hybrid Meetings: Experiences and Lessons Learned from an Interactive Workshop
Bibliographic record
Abstract
As demonstrated in recent years, telepresence technologies play a crucial role in the hybrid world. However, successfully carrying out hybrid activities that ensure participant engagement and equal opportunities for interaction and collaboration between in-person and remote participants still require significant effort. Building on this premise, this paper presents the methodology and lessons learned of a hybrid workshop involving eight in-person and eight remote participants. During the workshop, various telepresence technologies for hybrid meetings were tested, including 360-degree video-based systems and a telepresence robot. The workshop involved two main interactive activities: one focused on presentations to the audience (both local and remote), and a second focused on hybrid groups discussing a selection of provocative questions to compare and reflect on these technologies in terms of immersion, interaction capabilities, social implications, and practical convenience. In both activities, we ensured that all participants used the telepresence systems. This paper describes all the details that allowed us to successfully organize the workshop in terms of hardware, software used, roles assigned to organizers, and challenges faced. It also gathers the conclusions raised by both the participants and organizers to provide the community with valuable considerations for the design of future hybrid workshops, along with interesting insights obtained during discussions that highlight areas where future research should focus.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".