A Rapid Scoping Review on Academic Integrity and Algorithmic Writing Technologies
Bibliographic record
Abstract
This presentation provides insight into the development and findings of a rapid scoping review centred on the intersections of academic integrity and artificial intelligence, with particular attention to algorithmic writing technologies (e.g., ChatGPT) involving faculty, students, teaching assistants, academic student support staff, and educational developers in higher education contexts. This rapid scoping review was developed by a transdisciplinary team including Communication studies, Education, Engineering, and English, and followed Joanna Brigg Institute’s (JBI) updated manual for scoping reviews and the Preferred Reporting Items for Systematic reviews Meta-Analysis (PRISMA) reporting standards. JBI provides a high-quality, trusted framework for conducting these kinds of studies. This inquiry’s study design includes qualitative, quantitative, mixed methods, theoretical and opinion studies; additionally, this inquiry did not restrict studies by geographic location and focused on sources written in English. This review’s studies involved faculty, students, teaching assistants, academic support staff, and educational developers in higher education. It also included studies about artificial intelligence in the context of academic integrity, focusing on artificial intelligence tools that assist text generation and writing developed in Tertiary type A and B postsecondary education. Studies excluded from this review were related to primary and secondary education contexts, did not address the ethical implications of artificial intelligence, and focused on text plagiarism software. The protocol of this rapid review was published in the Canadian Perspectives on Academic Integrity Journal. Its implementation helped this team identify various ethical implications signalled by scholars between 2007 and 2022. Considering the expansive emergence of these technologies and the multiple positionings derived from these new and unprecedented encounters with such technology, we believe that the implications identified in this rapid scoping review are particularly relevant to inform academic staff, administration, students, and academic integrity researchers’ ethical decision-making and practices when teaching, learning, designing, and implementing assessments, and doing research. The findings of this rapid scoping review encompass nuanced perspectives concerning the ethical and unethical uses of these emerging technologies and insights into equity, diversity, and inclusion issues.
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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 | 0.235 | 0.482 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".