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

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.