Unlocking the Future: Exploring Generative Artificial Intelligence in Post-Secondary Chemistry Education with a Focus on Summative Assessment Applications
Bibliographic record
Abstract
Generative artificial intelligence (GenAI), specifically ChatGPT, has been gaining traction as a platform that allows users to do several tasks, from planning itineraries to designing logos, to answering questions. Its impacts have been felt across all sectors, including business, healthcare and academia. This systematic review paper focuses on the applications of AI in academia – specifically chemistry education. A comprehensive literature search examined all available articles as of April 2024. A total of 40 articles were found to relate to the theme of GenAI use in chemistry education. These articles were analyzed and mapped by category to provide an overview of GenAI usage and research in this subfield of academia. This resource could be of value to chemistry instructors and teaching assistants interested in exploring GenAI use in their teaching. Following the mapping exercise, the five papers regarding summative assessments were selected for a more in-depth critical review. These papers were evaluated for their approach to incorporating AI into post-secondary chemistry education, and for the author’s attitudes towards GenAI technology. While all author teams found similar shortcomings, such as inaccuracies in the GenAI outputs, how these shortcomings were viewed varied between the papers and shaped the authors’ views towards GenAI. Where some authors saw a problem, others saw an opportunity to leverage imperfect AI outputs to enhance critical thinking skills in their students. Future directions, such as developing more advanced AI models and exploring student engagement, are also discussed in this review.
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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 | 0.035 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".