Scientific research is undergoing transformation, driven by the rapidly growing adoption and integration of artificial intelligence (AI) in research and development (R&D) (Cockburn et al. 2019; OECD, 2023). AI promises to fundamentally change scientific practices at all stages, including ideation, hypothesis generation, experimentation, data interpretation, analysis, validation, and communication (Bianchini et al. 2022; Gottweis et al., 2025; Xu et al. 2021). These transformations span disciplines — from computer and physical sciences to life, social, and medical science (Van Noorden & Perkel, 2023). Laboratories, research centers, and their personnel are at the core of this shift, as research increasingly relies on AI-enabled tools and systems.

The AI in the Lab track examines how AI is reshaping R&D, focusing on the practices, methods, and epistemic norms emerging as AI becomes embedded in the production of new scientific and technological knowledge. It highlights AI-enabled transformations in the doing of science: how discovery is pursued, validated, and shared – whether in universities, public research organizations, or the growing number of corporate labs engaging in AI-enabled open research. The track recognizes that these developments occur amid broader transitions, including geopolitical uncertainty, funding constraints, and concerns about trust in science, that influence the context within which AI is designed and deployed in science.

We invite contributions that explore how AI is transforming upstream R&D in relation to:
• AI-enabled research design and methods – the use of machine learning, generative and other AI technologies in experimentation, data collection, and hypothesis generation.
• Human–machine interaction – the evolving relationship between human and machine intelligence in generating, validating, and interpreting knowledge.
• Instruments, data, and infrastructure – the integration of digital platforms, data ecosystems, and AI-enabled computational pipelines into laboratory practice.
• Scientific productivity and novelty – how AI-enabled tools and workflows influence research performance, discovery rates, and the originality or direction of scientific outcomes.
• Expertise and team composition – the emergence of hybrid skills and interdisciplinary collaborations between scientists and AI specialists.
• Ethics, openness, and responsibility – addressing questions of bias, explainability, reproducibility, and transparency in AI-enabled science discovery.
• Knowledge transfer and communication – exploring how AI-enabled science is changing how new scientific discoveries are shared and disseminated.
• Scientific convergence -- the emergence of novel hybrid science fields that integrate distant areas of knowledge through the boundary spanning applications of AI-enabled tools.
• Cross-sectoral and institutional collaborations – the interplay among university, other public, and corporate researchers and the changing landscape of international collaborations in AI-enabled research.

Through these perspectives, the track seeks to understand how AI is altering not only the technical processes of research but also the priorities, epistemic norms, and social relationships underpinning the scientific enterprise. The track invites studies of emerging AI-enabled forms of research organization, experimentation, and communication in academic and industrial settings, as well as their implications for open science, collaboration, and the future of scientific inquiry in an era increasingly shaped by AI systems. The track welcomes all methodological approaches, including qualitative and quantitative studies, and investigations across all disciplines of science and engineering.