Intelligent feedback polarity and timing selection in the Shufti Intelligent Tutoring System
Bibliographic record
Abstract
It is well known that the training of medical students is a long and arduous process. Students master many areas of knowledge in a relatively short amount of time in order to become experts in their chosen field. The Socratic Method used in the latter stages of medical education, where a physician directly monitors a group of students, is inherently restrictive due to the limited number of cases and length of the students’ rotations. Innovative Intelligent Tutoring techniques offer a solution to this problem. This paper outlines the overall structure and design of Shufti, an Intelligent Tutoring System (ITS) focused on mammography and medical imaging. Shufti's aim is to provide medical students with an improved learning environment, exposing them to a broad range of examples supported by customized feedback and hints driven by an adaptive Reinforcement Learning system and Clustering Techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".