A Study of Graduate Students’ Experiences of Artificial Intelligence at the University of New Brunswick
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly being used by students in higher education for a wide range of tasks, such as brainstorming, finding information, or drafting papers. While we understand the general use cases for AI in the classroom, there is a gap in the research about students’ processes for learning new tools, evaluating them, and implementing them into their learning processes. This talk summarizes the initial findings of focus groups conducted with graduate students at the University of New Brunswick which explored student perceptions of, and experiences with AI technology. Une étude des expériences des étudiants diplômés avec l'intelligence artificielle à l'Université du Nouveau-Brunswick RésuméL'intelligence artificielle (IA) est de plus en plus utilisée par les étudiants universitaires pour une vaste gamme de tâches, comme la recherche d'idées, la recherche d'information ou la confection d'un brouillon. Bien que nous comprenions les cas généraux propices à l'utilisation de l'IA dans les classes, il y a un manque dans la recherche à propos des processus des étudiants pour apprendre, évaluer et implémenter de nouveaux outils dans leurs environnements de travail. Cet exposé résume les résultats initiaux des groupes de discussion organisés avec des étudiants diplômés de l'Université du Nouveau-Brunswick, qui ont exploré les perceptions des étudiants par rapport—ainsi qu'avec—l'intelligence artificielle. Mots-clésBibliothèques universitaire; Intelligence artificielle
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".