Enhancing Research with Chatgpt: Seven Strategic Applications

The rise of artificial intelligence in academic research has introduced transformative opportunities to improve the efficiency, accessibility, and analytical depth of scholarly work. This study investigates the strategic integration of ChatGPT, an advanced generative AI model, into seven critical stages of the research data analysis lifecycle: data cleaning and structuring, exploratory data analysis, visualisation, statistical modelling, interactive tabulation, literature synthesis, and report generation. Drawing on empirical use cases and theoretical perspectives, we argue that ChatGPT functions not only as a computational tool but also as a cognitive collaborator capable of enhancing methodological rigor and interpretive clarity. This study demonstrates how the ChatGPT reduces technical barriers, fosters inclusive participation, and accelerates knowledge production across disciplinary boundaries. While the model offers significant pedagogical and epistemological benefits, we also identified ethical challenges related to AI bias, accuracy, authorship, and research integrity. The paper concludes by positioning ChatGPT as a partner in reimagining human–machine collaboration in the research enterprise and calls for critical frameworks to guide its responsible adoption in academic ecosystems. This study contributes to the ongoing debates on AI-assisted scholarship and provides a roadmap for integrating large language models into high-impact research.