ChatGPT in science and research: How generative AI drives innovation and knowledge development

The rapid rise of generative artificial intelligence (AI), particularly Chat Generative Pre-trained Transformer (ChatGPT), is transforming how knowledge is produced and innovation unfolds across disciplines. To capture this shift, we analyze 13,942 peer-reviewed publications with Latent Dirichlet Allocation, identifying 120 distinct research topics and grouping them into nine thematic clusters. Drawing on these clusters, we propose a four-lane conceptual model of “ChatGPT-driven value creation,” which portrays the AI pipeline as a sequential, feedback-linked process of knowledge conversion: (i) emerging technologies, (ii) data engineering and algorithmic development, (iii) perception-and-compliance filtering, and (iv) domain adoption. The model reveals where generative AI currently fuels innovation, most prominently in education, healthcare, and business, and where unresolved issues persist, including ethics, security, and misinformation. It also uncovers regional differences in research priorities, signaling diverse innovation dynamics across continents. Overall, by coupling large-scale topic mapping with a theoretically grounded architecture, this study offers scholars, policymakers, and practitioners a coherent framework for harnessing ChatGPT’s potential while addressing governance demands and quality constraints. Effectively, the conceptual model provides both a diagnostic lens for research and a roadmap for responsible, knowledge-intensive deployment of generative AI.