Transitioning simulated client interviews from face-to-face to online: Still an entrustable professional activity?
Bibliographic record
Abstract
This article is an analysis of what is still a relatively new educational method in law schools, namely the use of simulated clients (SCs) for learning and assessment in legal education. We outline the method and its origins, give a brief history of it in law schools to the present, and then focus on two modalities of the method, namely the face-to-face client interview and the online client interview. We discuss two instances of SC use, namely the adaptation of the method in professional education across four provinces in Canada; and the use made of the method by one law school in the province of Ontario, Canada. We discuss the social and educational contexts of the two modalities and relate it to other literatures emerging from the experiences of pandemic learning in higher education (HE). Finally, we draw some conclusions in the light of wider considerations of simulation, digital simulation, and the future of experiential education.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".