Generative AI in Undergraduate Education: An Early View of Developments, Prospects, and Challenges of the AI Revolution
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it