Navigating the Ethical Frontier: Graduate Students’ Experiences with Generative AI-Mediated Scholarship
Bibliographic record
Abstract
This qualitative study explores graduate students’ perceptions of using a generative AI-powered research application, COREI, and its impact on their sense of intellectual and scholarly ethics. Semi-structured interviews were conducted with graduate students ( n = 10), four doctoral and six masters’, from a large research university in Western Canada. Participants were given access to COREI for one month and encouraged to use its features in their research projects. Thematic analysis of the interview data revealed four main themes: (1) academic integrity and generative AI collaboration, (2) agency in the generative AI-assisted research process, (3) authorship and the personalization of AI-generated content, and (4) originality through generative AI-assisted research. Although some participants initially expressed concerns about the potential for AI to compromise academic integrity, many came to view COREI as a collaborative tool that, when used responsibly, could enhance their research without infringing upon their scholarly ethics. Participants emphasized the importance of human agency and decision-making in the AI-assisted research process, and the need for critical evaluation and personalization of AI-generated content to maintain authorship. Originality emerged as a collaborative feat between human expertise and AI’s generative capabilities. The findings suggest a need for reconceptualizing traditional notions of agency, authorship, and originality in the context of AI-assisted research. The study highlights the importance of developing ethical frameworks and institutional policies that prioritize human agency and critical engagement with AI-generated content, while also emphasizing the need for further research on the long-term impacts of generative AI on intellectual and scholarly ethics.
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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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.043 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".