AI, originality, and attribution: Researchers’ perspectives on distinguishing contributions
They face cognitive and practical challenges applying traditional integrity norms to AI-assisted work. Findings highlight the need for critical dialogue, reflective practices, and nuanced guidelines to uphold research integrity and thoughtfully integrate human value with machine capabilities.
Authors
- Ching‐Long Lin (ORCID: https://orcid.org/0000-0002-0344-5457)
- Yanyi Wu (ORCID: https://orcid.org/0009-0002-2948-0493)
- Xinyu Lu (ORCID: https://orcid.org/0000-0002-0282-122X)
Institutions
- Zhejiang University (CN)
Publication Details
- Journal
- Accountability in Research
- Published
- 2025-07-20
- DOI
- https://doi.org/10.1080/08989621.2025.2536817
- Citations
- 3
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- National Natural Science Foundation of China