AI Workroom: An Intelligent Platform for AI-Enhanced Meeting Experiences
Bibliographic record
Abstract
This paper introduces the creation of "AI-Workroom," a smart virtual conference app developed on TypeScript and driven by Zego Cloud APIs. The main aim of this project is to support remote collaboration, especially in working and learning environments. AI-Workroom supports basic virtual conferencing features like meeting setup and entry, screen sharing, video and audio communication, and real-time texting . Some of the key highlights in the new system is integrating an AI workspace, completing a meeting and people with AI capability to create images and a whiteboard to utilize visual explanations to present something. The AI image creation module allows users to create images for specified subjects and topics in real-time that drastically lowers the necessity of finding images outside, this module was created on the foundation of the Hugging Face API. The interactive whiteboard contains simple drawing instruments such as a pencil, eraser, and text box which allows teachers and presenters to represent ideas and thoughts in an instant. Robust user authentication and data management is done via Firebase, with Google Authentication providing secure access. AI-Workroom seeks to revolutionize the model of engagement between teachers and students in virtual space integrated with visual aids
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".