Human-AI Research Creation Framework (HARCF): A Qualitative Evidence Synthesis Collaboration in Scientific Research

Objective: This study aims to investigate how Generative Artificial Intelligence (GenAI) contributes to the creation of scientific knowledge throughout the research lifecycle, as well as to develop the Human-AI Research Creation Framework (HARCF) as an integrated conceptual framework that explains human-AI collaboration in scientific research.
Research Design & Methods: This study employed a Qualitative Evidence Synthesis (QES) approach to analyze 17 empirical articles using open coding, axial coding, selective coding, cross-case synthesis, and theme development to identify patterns of human–AI collaboration and construct a conceptual framework.
Findings: The research findings indicate that the role of GenAI has shifted from merely a writing aid to a collaborator in the creation of research. Six main themes were identified: AI as a Research Ideation Partner, Knowledge Exploration and Synthesis, AI-Supported Research Design, Iterative Human–AI Co-Creation, Human Validation and Critical Judgment, and Responsible AI Governance. Based on this synthesis, HARCF was developed as a framework that describes the creation of research as a collaborative, iterative process that remains under human epistemic control.
Implications: HARCF provides conceptual guidelines for researchers, academic institutions, and journal editors on how to implement AI responsibly while maintaining transparency, research integrity, and human oversight.
Contribution & Value Added: This study proposes HARCF as a new conceptual framework that shifts the paradigm from AI-assisted writing to human–AI research creation, while also expanding Knowledge Creation Theory through the concept of human–AI co-creation as a new mechanism for the creation of scientific knowledge.