AI-Driven Transformation of High-Resolution X-ray Diffraction: Data Infrastructures, Reproducibility, and Educational Implications

The integration of artificial intelligence (AI) into experimental physics is reshaping the infrastructures, methodologies, and educational practices that support scientific inquiry. High-resolution X-ray diffraction (HRXRD) provides a compelling case study, as its intricate geometries, multidimensional parameter spaces, and large data volumes make it both a driver and beneficiary of AI-based innovation. This paper examines the transformations emerging from AI-enabled HRXRD across three interconnected domains. First, we analyze how standardized, FAIR-compliant data infrastructures—including metadata frameworks, interoperability standards, and real-time streaming systems—enable the effective deployment of AI algorithms during experiments. Second, we address the challenges that algorithmic decision-making poses for reproducibility and transparency, emphasizing the need for systematic logging, model sharing, and explainable AI (XAI) approaches to maintain scientific rigor. Third, we explore the educational opportunities arising from these developments, ranging from virtual laboratories and remote experimentation to the cultivation of data-intensive and critical AI literacy. Taking together, these dimensions outline a pathway toward a new experimental culture in which human expertise and algorithmic intelligence interact transparently and reproducibly. The paper argues that aligning AI integration with open data practices and pedagogical innovation can strengthen both the epistemology and the teaching of contemporary experimental physics.