AI, originality, and attribution: Researchers' perspectives on distinguishing contributions

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.

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.

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.

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.