Employing AI to Help Evaluate Accumulating Student Perspective in Curricular Design
Bibliographic record
Abstract
Curricular design efforts benefit from system implementation for both development and ongoing improvement. A project-goal framework was selected to interconnect curricular efforts with artifacts of learning that represent graduate attributes across-curriculum. These artifacts are adjudicated in correlation with prepared rubrics to guide assessment in connection with set learning outcomes. While artifacts are a good metric to show learning outcomes in the eyes of the educator and accreditation boards, there are questions remaining if this perspective of learning is shared by the learners themselves, and if improvement in focus can be made by employing a LLM AI. Comparison of student surveys were correlated with machine-learning generated association of assignment rubrics to both course and program learning objectives. The results indicate that while both students and instructors share a similar perspective of what is going on in the classroom, these new AI tools could be used to improve the interconnection of learning goals to artifact requirements.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".