Research in the Era of AI: Opportunities, Challenges, and New Frontiers

Artificial Intelligence (AI) is changing the way scientific research is carried out and the way we teach and train new generations of researchers. Tasks that used to be slow and largely manual, from reviewing the literature to writing code and drafting papers, can now be supported by AI tools, while data-driven methods are increasingly present across many stages of the research process. This panel session brings together researchers, educators, and practitioners to discuss how these developments are changing the way we produce, validate, and disseminate knowledge. The discussion will span three connected dimensions. First, how we do research: generative models and large language models (LLMs) are speeding up literature review, hypothesis generation, code writing, and even model formulation, while data-driven methods increasingly complement classical exact and heuristic approaches to optimization. Second, how we publish and evaluate: AI-assisted writing, automated peer review, and concerns around reproducibility, authorship, transparency, and research integrity are challenging established academic practices and leading communities to reconsider their standards. Third, how we teach and train the next generation: students now learn, code, and solve problems alongside AI tools, raising important questions about which skills remain essential, how to preserve critical thinking and methodological rigor, and how to encourage responsible use rather than uncritical dependence. The aim of the panel is not to provide definitive answers, but to open a discussion on the opportunities that AI offers to the research and academic community, the risks it introduces, and the new questions it raises for how we work, publish, and teach. Together, these reflections will help us think about how research and education may evolve in the coming years, and how we can make good use of these tools while preserving rigor, critical thinking, and integrity.