Artificial Intelligence in Higher Education: Examining the AI Policy Landscape at U.S. Institutions
Bibliographic record
Abstract
Addressing a critical need for comprehensive policy development in the area of Artificial Intelligence in Education (AIED), this exploratory study investigates the prevalence and characteristics of emerging AI policies across U.S. higher education institutions. Through a close review of AI policies from a stratified random sample of four-year institutions across the country, variations in policy adoption were examined across different institution types. Official online policy-related resources provided for campus-wide use were observable in less than half of U.S. institutions, and large discrepancies in the quality and quantity of policy guidance provided were found among existing policy resources. Significant disparities were most associated with institution type. Research-intensive universities were more likely to have provided significant AI policy resources compared to smaller institutions. The study identifies a range of policy providers and highlights diverse approaches to AI use, ethical guidelines, and target audiences, which reflect different combinations and degrees of leadership, institutional cultures, resources, and individual initiatives. Considering the social impacts of technologies, these varied results underscore the necessity for a standardized and well-structured policy framework to ensure effective and ethical AI integration in higher education. The lack of consistency in existing policy practices, especially in light of emerging ethical considerations, calls for urgent, multi-disciplinary policy development to harness AI’s potential while mitigating its risks. Implications for future AI policy research, development, and application are discussed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".