Software Training in High Energy Physics for the AI Era

High Energy Physics (HEP) is a deeply collaborative and software-driven discipline, where scientific discovery depends on advanced computing, data analysis, simulation, and software engineering. Large-scale experiments across many facilities produce unprecedented volumes of data that demand sophisticated computational infrastructure and highly skilled researchers who can build, maintain, and use complex software ecosystems to find rare physics signals. Software expertise has become a core scientific competency rather than a supporting technical skill. The rise of machine learning, artificial intelligence, cloud computing, and increasingly autonomous research tools further widens the skills physicists need. Building sustainable, accessible software training is therefore key to reproducible science, international collaboration, research productivity, and a diverse workforce ready for the computational challenges of future scientific facilities.

This Research Topic explores how software training can equip current and future researchers with the computational, data science, and AI skills modern HEP requires. Although collaborations have advanced software infrastructure, many researchers still struggle to gain software engineering competencies, adopt reproducible workflows, and use emerging AI tools. Training often remains fragmented across experiments and rare at the institutional level, producing uneven access to resources and limited evaluation of outcomes.

Recent developments in generative and agentic AI bring both opportunities and challenges. These tools can assist with code development, workflow automation, literature exploration, data analysis, and documentation, but they raise pressing questions about validation, reproducibility, transparency, and responsible use. We seek contributions that identify effective ways to integrate modern software practices and AI-enabled tools into scientific training. By bringing together physicists, software developers, educators, computing specialists, and AI researchers, this collection aims to establish best practices for using AI in physics analyses, validating results, ensuring ethical use, and taking responsibility for the agents deployed, strengthening research capabilities while maintaining scientific rigor, reliability, and reproducibility.

We welcome original research, reviews, perspectives, educational innovations, case studies, and community reports on software training and workforce development in HEP and related computational sciences. Contributions may address the design, implementation, evaluation, and sustainability of training programs across all career stages.

Topics of interest include, but are not limited to:
· Software engineering education and computational thinking for scientific research
· Training in Python, C++, scientific workflows, and analysis frameworks
· Reproducible, sustainable software development; version control, testing, documentation, and continuous integration
· Data science, statistical computing, and large-scale data analysis
· Training for cloud, distributed, and high-performance computing
· Machine learning and AI education for HEP
· Community-developed curricula and open educational resources
· Assessment of training effectiveness and learner outcomes
· Mentoring, peer-learning, and collaborative training models
· Diversity, equity, inclusion, and accessibility in scientific software training
· Workforce development and transferable computational skills

Authors encouraged to address emerging best practices for agentic AI in scientific workflows, including:
· Human oversight and verification of AI-generated results
· Reproducibility and auditability of AI-assisted analyses
· Validation and benchmarking of AI-generated code
· Transparent reporting of AI use in publications and research products
· Multi-agent systems for simulation, analysis, and data management
· Ethical and trustworthy AI practices in scientific research
· Integration of AI assistants into HEP training curricula
· Community standards and governance for AI-enabled science

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Resposnible AI, igh Energy Physics, Software Training, Scientific Computing, Research Software Engineering, Data Science, Reproducible Research, Software Sustainability, Machine Learning, Artificial Intelligence, Agentic AI, AI for Science, High-Performance Computing, Scientific Workflows, Workforce Development, Open Science

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.