Balancing Efficiency and Depth: A Systematic Review of Artificial Intelligence’s Impact on Research Competencies Across the Research Lifecycle

Background: The integration of artificial intelligence (AI) into academic research is fundamentally transforming how scholarship is conducted across disciplines. This systematic review synthesizes empirical and conceptual literature that examines how AI tools, particularly large language models and machine learning systems, are reshaping essential research competencies throughout the research lifecycle. Methods: We analyzed 49 studies across various disciplines, methodologies, and geographic regions to assess AI's impact on research processes, from literature review to knowledge dissemination. Our framework evaluated the effects across three dimensions of research competency: technical, critical, and social, while integrating established theoretical perspectives, including Mode 2 Knowledge Production and Distributed Cognition theory. Results: Our findings reveal dramatic efficiency improvements in research processes, with a 50-95% reduction in workload for literature screening and 70-80% savings in time for qualitative analysis. However, these gains introduce significant tensions: between efficiency and interpretive depth, expertise and automation, reference accuracy and research integrity, and equal access versus emerging "research divides." The impact varies by research stage, with literature review, qualitative analysis, and hypothesis generation undergoing the most substantial transformations. Discussion: Our conceptual framework demonstrates how AI integration represents not merely technological adoption but a fundamental reconceptualization of research expertise—from technical execution toward critical judgment, ethical reasoning, and interdisciplinary integration. We propose balanced human-AI collaboration models that emphasize strategic human oversight, transparent documentation practices, and stage-appropriate automation. These findings have significant implications for research education, institutional policy, and the future development of research competencies in an increasingly AI-mediated knowledge ecosystem.