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AI is changing how we think about research

The study investigates the growing role of AI systems like ChatGPT in the world of academic research. These tools can do everything from summarizing literature to helping design experiments and draft papers. Researchers are excited about how these tools can speed up research, but there are concerns about the risks, such as factual errors and misrepresentation in AI-generated content. Another important issue is the question of authorship—who is responsible for a research paper when much of it is created by AI?

The purpose of this study

This study aimed to explore the implications of AI's growing role in the research process. The authors focused on the challenges and opportunities AI presents, particularly in management and organization studies. Their goal was to understand how AI might change key areas of research, including authorship, originality, and the power dynamics involved in producing new knowledge.

Elmira van den Broek contributed to this curated discussion by focusing on how new hybrid forms of research emerge as researchers reflect and adapt their work practices in interaction with AI. 

Key research findings

• AI tools can significantly speed up research—but they also introduce risks like factual inaccuracies and biases, potentially undermining the credibility of academic work.
• The rise of AI forces us to rethink authorship. If machines generate large parts of a paper, can they be credited as co-authors? Or does this diminish the role of human intellectual contributions?
• AI may stifle true innovation. By relying on existing data, AI tools could recycle old ideas rather than generating groundbreaking new insights, potentially leading to an era of safe, predictable research outputs.

What this research means for the future

 This research highlights the urgent need for ethical guidelines in the academic use of AI. If AI continues to take over more research tasks, there’s a real danger that human creativity and critical thinking will be overshadowed by machine efficiency. Future research must focus on how to ensure responsible AI use and draw clear lines between human and machine contributions. The study also raises broader questions about the future of industries and education. As AI becomes more pervasive, will students still develop the critical thinking skills they need? And if innovation depends on AI recycling old ideas, how will industries that rely on cutting-edge breakthroughs remain competitive?

Meet the researchers

• Mukta Kulkarni: Indian Institute of Management Bangalore, Bangalore, India
• Saku Mantere: McGill University, Quebec, Canada
• Eero Vaara: University of Oxford, Oxford, UK
• Elmira van den Broek: Stockholm School of Economics, Stockholm, Sweden
• Stella Pachidi: University of Cambridge, Cambridge, UK
• Vern L. Glaser: University of Alberta, Alberta, Canada
• Joel Gehman: George Washington University, Washington, DC, USA
• Gianpiero Petriglieri: INSEAD, Fontainebleau, France
• Dirk Lindebaum: Grenoble Ecole de Management, Grenoble, France
• Lindsey D. Cameron: Wharton School, University of Pennsylvania, Pennsylvania, USA
• Hatim A. Rahman: Kellogg School of Management, Northwestern University, Illinois, USA
• Gazi Islam: Grenoble Ecole de Management, Grenoble, France
• Michelle Greenwood: Monash University, Victoria, Australia​.