Creating a Joint-Faculty Artificial Intelligence Concentration within a Graduate Program
Bibliographic record
Abstract
The global demand for Artificial Intelligence (AI) talent is growing at a rapid pace, leading to significant AI skills shortages. This experience report describes the creation of a new and uniquely joint-faculty AI concentration within our existing Master's program, characterized by its applied research internship in industry. We describe the experience of creating a multi-disciplinary AI program through broad consultation across two of the largest faculties in our institution. The new concentration has been well-received by the collaborating faculties and with industry partners; its popularity with applicants has resulted in admitting exceptional candidates from around the world and becoming the most popular concentration in the program. We offer this experience report in the hope that it may serve as a model for other practitioners who are considering navigating the creation of a joint-faculty concentration within a graduate program, especially in the popular field of AI.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".