Integrating Artificial Intelligence into PhD Research Methodology: A Meta-Synthesis and Framework Development

The present study was conducted with the aim of developing a framework for integrating artificial intelligence into research methodology education at the doctoral level. This study is applied in purpose and qualitative in nature, grounded in the meta-synthesis theoretical approach. The research population consisted of scientific studies and articles related to the topic, published between 2010–2025 and 1395–1404. Data were collected through a systematic review and analysis of credible national and international databases, including Magiran, Jihad Daneshgahi, Google Scholar, SAGE, and Web of Science. During data analysis, NVivo software was employed for coding and extracting themes. After analyzing the content of 80 selected articles, five main themes were identified. These included: educational infrastructure and support, research ethics and transparency, AI and data literacy, integration processes based on methodological alignment, and desirable skill-based research outcomes related to doctoral research competencies.
The findings indicate that the effective and responsible integration of generative artificial intelligence in research methodology education requires clear policies, robust computational infrastructures, systematic user training, and ethical and human oversight. Implementing these elements can enhance research quality, critical thinking, data literacy, and doctoral students’ research capabilities. The results of this study provide a theoretical foundation for developing educational strategies and policy frameworks aimed at empowering researchers and advancing higher education systems in the age of artificial intelligence.