Preparing Students for the Generative Artificial Intelligence Future: Integrating Artificial Intelligence Literacy in Sport Management Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As artificial intelligence (AI) becomes increasingly embedded in everyday life, there is a growing expectation that graduates are savvy in utilizing these tools. However, arguably, more important than simply understanding how to utilize these tools is fostering AI literacy, that is, developing the skill to utilize AI tools and the ability to critically assess their utility. This paper introduces an assignment that utilizes ChatGPT to promote critical engagement with course content and a generative AI tool. By engaging students in tasks that require interaction with AI tools like ChatGPT, educators can nurture both technical skills and the ability to discern the appropriate application and limitations of AI, an essential skill for preparing students for the complexities of the modern technological landscape.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it