Fueling Conversations: <scp>AI</scp> Education across the <scp>iSchools</scp> in the <scp>US</scp> and Canada
Bibliographic record
Abstract
ABSTRACT Artificial Intelligence (AI) has become prevalent in all sectors of society, including higher education institutions. Many studies have examined iSchools curricula, focusing on areas such as data science, digital humanities, and archival studies. However, few studies have examined AI education at iSchools in the United States (US) and Canada. Research is needed to address AI in information science (IS) education, fueling the conversation about AI across the iSchools' curricula. This study analyzed the AI‐related courses in graduate and undergraduate programs offered by members of the iSchools organization in the United States and Canada. We identified the area(s) and facet(s) covered in each course title and coded them. Of the 51 iSchools, twenty‐nine offered AI‐related courses. The most covered areas include general AI, Machine Learning, Natural Language Processing, Deep Learning, and Robotics. Most courses focus on AI's technical and applied facets, while a few cover the ethical, societal, cultural, and legal facets. Implications include the need for iSchools to offer AI courses that cover aspects beyond the technical, more undergraduate courses, and certificate programs that contribute to educating the labor force that needs upskilling. Drawing from empirical data, this study informs the iSchools' curricula strengths to build on and the gaps to fill and has implications for IS practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".