Incorporating ChatGPT into a Simulated Peer-Review Assignment in an Upper-Level Materials Chemistry Course
Bibliographic record
Abstract
The growing presence of generative artificial intelligence (GenAI), such as ChatGPT, has already begun to alter the scientific landscape. In addition to the utility of GenAI, there are also concerns about its ethical use. The rapid introduction of GenAI means that science undergraduate curricula need to be updated to address this technological evolution. To that end, we report here an assignment for an organic materials chemistry course incorporating ChatGPT. This two week activity involves students using ChatGPT to generate short essays on course topics and then critiquing and editing the generated information. Students reported that this assignment helped their understanding of the course topics and that they enjoyed the activity overall. Additionally, we address some of the issues we encountered when implementing this assignment due to the unpredictable nature of ChatGPT, and the solutions we found for them. Overall, students felt that this assignment was valuable for improving their grasp of the course topics without an excessive time commitment.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".