Using generative artificial intelligence in chemistry education research: prioritizing ethical use and accessibility
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) has the potential to drastically alter how we teach and conduct research in chemistry education. There have been many reports on the potential uses, limitations, and considerations for GenAI tools in teaching and learning, but there have been fewer discussions of how such tools could be leveraged in educational research, including in chemistry education research. GenAI tools can be used to facilitate and support researchers in every stage of traditional educational research projects (e.g. conducting literature reviews, designing research questions and methods, communicating results). However, these tools also have existing limitations that researchers must be aware of prior to and during use. In this research commentary, we share insights on how chemistry education researchers can use GenAI tools in their work ethically. We also share how GenAI tools can be leveraged to improve accessibility and equity in research.
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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 | 0.275 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.034 | 0.033 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".