Artificial Intelligence Meets Academic Integrity: Evaluating AI Tools To Support Literature Reviews
Bibliographic record
Abstract
Every research project and almost every scholarly paper begins with a literature review. A growing number of artificial Intelligence (AI) tools can be used to expedite various steps of the review process, including problem formulation, literature search, screening for inclusion, quality assessment, data extraction, and data analysis and interpretation. The process of conducting literature reviews and the quality of the results obtained may vary according to the AI tools a researcher employs, however, and the academic integrity remains a paramount concern. In this symposium we bring together five panelists, each an expert in the development and/or use of some AI tools, to help establish the state-of-the-art re: AI research tools for reviews and to debate the validity, utility, and ethics involved in using them. Two discussants, well-known for their expertise regarding the effective and ethical conduct of systematic reviews, will emphasize the academic integrity and standards for rigorous and trustworthy reviews and reiterate researchers’ responsibilities in this regard. Overall, this symposium aims to describe and critique available and emergent AI tools and evaluate their alignment with the guidelines researchers must follow when conducting literature reviews.
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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 | 0.901 | 0.962 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.058 | 0.041 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.060 | 0.041 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".