2025/07/20 by Yanyi Wu, Xinyu Lu, Chenghua Lin · 2 voices · 1 citation
Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Ethics and Social Impacts of AI #Academic integrity and plagiarism
paper · doi:10.1080/08989621.2025.2536817
openalex publication_date 2025/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/15
BACKGROUND: Artificial intelligence (AI) is increasingly integrated into research, significantly challenging established scholarly norms around originality, contribution, and authorship. While policies are developing, there is a gap in understanding how individual researchers subjectively perceive and navigate these ambiguities in practice, impacting research integrity. METHODS: To explore researchers' perspectives on distinguishing human versus AI contributions, we conducted semi-structured interviews with 18 researchers (PhD student, Postdoctoral Researcher, Faculty) across diverse disciplines (STEM, Social Sciences, Humanities). Data were analyzed via reflexive thematic analysis, informed by Attribution Theory. RESULTS: Researchers predominantly conceptualize AI as a sophisticated tool requiring significant human direction, rather than a genuine collaborator. To navigate attributional ambiguity, they rely on subjective heuristics - such as "gut feelings" of ownership and using the labor of the research process as a proxy for conceptual contribution. This creates significant ethical tensions and a desire for clearer, more nuanced guidelines. CONCLUSION: 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.