Intelligent Tutoring Systems for Adaptive Learning Pathways in Healthcare Training
Bibliographic record
Abstract
Effective training is essential for successful surgical outcomes, yet traditional methods are often resource-intensive and inefficient. This study explores the use of AI-powered Recommender Systems to provide personalized and scalable training in robotic-assisted surgery (RAS) by offering adaptive task choices. The developed Recommender System divides the task selection process into two steps: a decision base and a decision algorithm. For the decision base data is collected and further enriched by artificial intelligence. This decision base is then used by the decision algorithm, an RL agent, which selects the next task with the aim of accelerating the student's learning. A synthetic dataset based on the Item Response Theory Knowledge Tracing Model was used to simulate task interactions and learning progress for individuals with varying skill levels across tasks of differing difficulty and requirements. Results show that a graph-based knowledge tracing model, which reveals latent structures among tasks, effectively supports the decision basis, while reinforcement learning enhances task selection within the decision algorithm. This framework demonstrates a promising approach for AI-driven RAS training, with future research focused on optimizing these components and preparing them for real-world implementation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".