Artificial Intelligence in Education Research: A Cross-Sectional Review of Applications, Competencies and Challenges Across the Research Lifecycle

Artificial intelligence (AI) is becoming more and more integrated into research as well as how students and early-career researchers are trained to do research. A cross-sectional, narrative review of AI applications throughout the research education lifecycle—including literature discovery, study design, academic writing training, and the growth of research competence and self-efficacy—is provided in this paper. The study, which draws from recent cross-sectional surveys, systematic reviews, and empirical research (2023–2026), consistently finds evidence that AI tools can speed up the screening of literature, help with writing clarity and structure, and, when applied thoughtfully, improve graduate and undergraduate researchers' critical thinking and research self-efficacy. However, the evidence base also brings up persistent issues with academic integrity, over-reliance that could undermine autonomous thought, erroneous or fake results, and unequal preparedness among students, instructors, and research supervisors. The study comes to the conclusion that AI's value to research education is more dependent on whether or not its application is purposefully designed to enhance rather than replace the methodological and reasoning skills at the core of research training.