Can't Help Myself
On generative AI, the performance of qualitative research and slow scholarship
Bibliographic Data
| ID | 2404408 |
|---|---|
| Authors | Ryan Nolan (0000-0003-2109-6008, University of Exeter, corresponding author) |
| Year | 2025 |
| Volume | 25 |
| Issue | 6 |
| Pages | 1473-1479 |
| Publication date | 2025-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Qualitative Research (JOURNAL) |
| Journal identifiers | ISSN: 1468-7941 • E-ISSN: 1741-3109 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/14687941241308696 |
| OpenAlex | W4406130944 |
| Language | EN |
| Citations received | 2 |
| References cited | 6 |
This article navigates the confluence of advanced generative AI tools, the intensifying demands of academic research, and the concept of slow scholarship. It employs the artwork 'Can't Help Myself' by Sun Yuan and Peng Yu as a metaphor to discuss the challenges and opportunities faced by researchers in this rapidly changing landscape. The discussion explores the surveillance nature of AI tools, the evolving demands of academia, and the implications for conducting qualitative research. The article proposes a synthesis where AI tools can be harnessed to enable depth and deliberation in research, echoing the principles of slow scholarship. This perspective helps alleviate the fear of being overwhelmed by AI and shifts the focus to constructive engagement with these powerful tools
Constructive · Deliberation · Engaged scholarship · Engineering ethics · Epistemology · Generative grammar · Metaphor · Political science · Qualitative research · Scholarship · Social science · Sociology · Computational and Text Analysis Methods · Computer Science · Data Analysis and Archiving · Engineering · Qualitative Research Methods and Ethics · Artificial Intelligence
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |