Embracing Artificial Intelligence: Incorporating Artificial Intelligence Into Classroom Instruction
Bibliographic record
Abstract
Background Instructors used generative artificial intelligence (AI) as a teaching tool in a third-year baccalaureate nursing leadership course to help students understand and critique a change management proposal. Method Instructors used generative AI to develop a sample section of a change proposal for students to critique in class followed by a class discussion. Results Using generative AI enabled instructors to quickly develop a sample section of a change proposal for students to critique. During this learning activity, students recognized the importance of verifying information generated by AI sources for accuracy with evidence-informed sources. Students reported that critically appraising the sample provided clarity on the assignment. Conclusion Leveraging generative AI in the classroom is a time-effective way for instructors to create learning activities for students, clarify the expectations for the assignment, and promote the importance of verifying information from AI sources. [ J Nurs Educ . 2025;64(7):e83–e84.]
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".