Generative AI in Undergraduate Education: An Early View of Developments, Prospects, and Challenges of the AI Revolution
Bibliographic record
Abstract
Across all disciplines, generative artificial intelligence (GenAI) threatens student academic integrity in traditional assessments. Its detection is unreliable. From talking with students, however, we know they are finding GenAI to be helpful in their studies. Through experiments and experience at five universities and colleges in the United States and Canada, this article demonstrates that GenAI can be strategically, thoughtfully, and critically deployed to improve postsecondary geography teaching and learning. Our experiments show that faculty can potentially create more efficient workflows by using GenAI to create assignments, multiple-choice questions, rubrics, and generalized feedback on assignments. We stress that GenAI output needs to be checked, but the time saved can be used to foster deeper student understanding and engagement with geographic concepts, and to assist students who are struggling. At the same time assessments need to be reimagined to incorporate the new realities of GenAI and we provide an example “spot the mistake(s)” type of question. Students and faculty need to be educated on the new technologies, not just for educational use, but as students move into careers.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".