Research in the Age of AI – How to Plan? A Bibliometric Exploration of the Research Lifecycle

Abstract

Artificial intelligence (AI), particularly generative AI and large language models (LLMs), is rapidly transforming research planning by enhancing literature synthesis, research problem formulation, methodology selection, and workflow management. Despite this rapid adoption, limited evidence exists regarding the intellectual structure, thematic evolution, and responsible integration of AI within research planning. Therefore, this study systematically maps the scientific landscape of AI-assisted research planning through a bibliometric analysis of publications indexed in the Scopus database between 2017 and 2026. Bibliographic data were analysed using Biblioshiny (Bibliometrix) and VOSviewer to examine publication trends, influential contributors, collaboration networks, intellectual structure, and thematic development. The findings reveal a rapidly expanding research domain, with an annual publication growth rate of 51.08%, reflecting the increasing integration of AI into research planning. Science mapping identifies research management, artificial intelligence, machine learning, and large language models as the dominant research themes, while thematic evolution demonstrates a shift from foundational AI technologies towards AI-enabled research management and workflows. However, the analysis also reveals a significant conceptual imbalance, with technological advancements outpacing the development of governance, trust, ethics, and human–AI collaboration. To address this gap, the study proposes a five-phase framework comprising Strategic Conceptualisation, Systematic Literature Synthesis, Methodology Selection, Governance, Ethics and Trust, and Continuous Reflection and Feedback, positioning the human touch, curiosity, critical thinking, creativity, contextual expertise, and ethical judgement, as the foundation of responsible AI-assisted research planning. The study contributes both a comprehensive knowledge map and a human-centred conceptual framework that provide practical guidance for researchers, scholars, and policymakers seeking to promote the responsible, transparent, and sustainable integration of AI throughout the research planning process.