Inventing with Machines: Generative AI and the Evolving Landscape of IS Research

• 成果类型: Article
• 署名作者: Gopal, Ram D.; Li, Jingjing; Riemer, Kai; Sarker, Suprateek; Singh, Param Vir; Susarla, Anjana; Bichler, Martin; Thatcher, Jason Bennett
• 署名单位: University of Warwick; University of Virginia; University of Sydney; Carnegie Mellon University; Michigan State University; Michigan State University's Broad College of Business; Technical University of Munich; University of Colorado System; University of Colorado Boulder
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2025.editorial.v36.n4
• 发表日期: 2025-12
• 关键词: generative AI; large language models (LLMs); automation of invention; AI usage levels in scholarship; responsible AI in research

摘要

Generative artificial intelligence (AI) is not merely changing how information systems (IS) research gets done-it is reshaping what research can be. We stand at a pivotal moment where machines can help generate hypotheses, synthesize vast literatures, and identify patterns that would take human researchers months to uncover. Yet, this unprecedented capability presents equally unprecedented risks to scholarly integrity. Because the field is uniquely positioned to understand sociotechnical transformations, IS research faces an extraordinary opportunity to pioneer inventing with machines while preserving the human insight and oversight that gives scholarship, as currently defined, its meaning. This transformation demands more than tool adoption. It requires a reimagination of scholarly infrastructure, norms, and practice. However, this transformation of research tooling creates a dangerous paradox: Powerful AI tools are now accessible to researchers who lack the technical literacy to understand and use them responsibly, threatening everything from citation accuracy to theoretical validity. Yet within this paradox lies the potential for revolutionary advances in how we craft our future as scholars. Informed by the sociotechnical perspective, we argue that the path forward requires coordinated community action that goes far beyond individual skill development. The IS community must lead the development of specialized AI tools that consider our theoretical traditions, create educational frameworks that preserve scholarly values while embracing computational capabilities, and pioneer review processes that harness AI's analytical power without ceding human control, at least, in the short run. Success will determine not only the future of IS scholarship but our field's capacity to guide other disciplines through this fundamental transformation of academic practice. The era of human-AI collaboration in research has already begun. How we govern and guide it will define the next generation of scholarly discovery.