Turk Talk: human-machine hybrid virtual scenarios for professional education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Virtual scenarios provide a means for creating rich and complex online cases for health professional students to explore. However, the response options available to the learner are usually predefined, which limits the utility of virtual patients. Using artificial intelligence or natural language processing to accommodate such flexibility is expensive and hard to design. This project description lays out an alternative approach to making virtual scenarios more adaptable and interactive. Using OpenLabyrinth, an open-source educational research platform, we modified the interface and functionality to provide a human-computer hybrid interface, where a human facilitator can interact with learners from within the online case scenario. Using a design-based research approach, we have iteratively improved our cases, workflows and scripts and interface designs. The next step is testing this new functionality in a variety of situations. This report describes the pilot implementation of this pilot project. It includes the background, rationale, objectives, learning and educational designs, and implications for software development. The costs and time required to modify the software were much lower than anticipated. Facilitators managed text input from multiple concurrent learners. Learners noted a delay while waiting for the facilitator's response, but denied becoming frustrated. The implementation and use of this new technique seems promising for training and assessment purposes related to developing effective communication skills. This report also explores the provisional implications arising from the study so far.
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.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it