The Evolving Roles of Humans and AI in Research

Abstract: Large language model (LLM)-based agents can increasingly support and empower human scientists in their work by reading literature, generating hypotheses, designing experiments, analyzing data, interpreting results, and drafting manuscripts. In parallel, robotics and programmable, cloud-connected laboratories have the potential to transform wet-lab operations into software-addressable infrastructure. Yet biological experiments face a persistent reality gap in which silent failures, temporal drift, contamination, sample mix-ups, and ambiguous protocol intent can invalidate an otherwise sound experimental plan. Closing this gap is essential for bringing the speed of modern computational discovery to experimental biology and requires audit-grade execution and verification. Here, we distinguish scripted automation from flexible, AI-enabled autonomy and outline a staged roadmap toward human-led and trustworthy “self-driving” labs for biology. We define laboratory autonomy in the context of a human-led scientific endeavor as a bounded control loop in which scientists set goals and constraints, agents help plan and coordinate experiments, instruments execute experiments and report machine-checkable evidence, and humans verify and interpret results and govern high-consequence decisions. We discuss where such systems are most appropriate and relevant today and where caution or deferral is needed. We map key lab-side potential failure modes and argue for verifiability-first autonomy. If developed responsibly, such systems could enable scientists to more efficiently decipher combinatorially large perturbation, design, and optimization spaces that are currently impractical to test experimentally, while empowering, rather than replacing, human creativity and judgment in experimental biological discovery.

Abstract: The rapid advancement of AI is fundamentally disrupting research and engineering. While much attention is given to how AI may optimize wireless systems, this article explores a different question: how will AI impact the ecosystem and community developing future wireless technology? We trace this transformation across the entire lifecycle, from education and core research to technical publication and production-ready network deployments. As AI increasingly automates routine tasks, the primary value of the human researcher will shift from problem-solving to problem-finding, research orchestration, and oversight of trade-off management. By actively preserving spaces for deep, unplugged thinking and steering AI toward genuine discovery rather than mere recombination, we can navigate this profound shift to ultimately elevate human ingenuity and the future evolution of the researcher.

Abstract: Artificial intelligence (AI) is increasingly integrated into scientific discovery processes, such as protein design, gene analysis, and materials research, significantly enhancing the efficiency of discoveries in these fields. While much recent literature emphasizes fully automated pipelines, it is crucial to acknowledge that scientific discovery is inherently a creative and high-stakes endeavor. Therefore, it relies heavily on human expertise for judgment and guidance, especially in the face of uncertainty. Despite rapid growth in human-in-the-loop and collaborative systems, the field lacks a unifying survey that explains how humans and AI actually collaborate across the scientific discovery life-cycle. In this paper, we present a systematic review of human–AI (HAI) collaboration for scientific discovery. Specifically, we have identified four representative roles of humans and AI. Using this lens, we then distill common HAI collaboration patterns across three distinct stages in the scientific discovery process (i.e., observation, hypothesis, and experiment). Finally, we identify key gaps in existing approaches and outline future research directions for developing trustworthy, role-aware human–AI systems in scientific discovery.

Abstract: 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.