Teaching Reproducible Science Through Software Engineering and Generative AI: A Training Paradigm for Emerging Researchers

Reproducibility in scientific research hinges on a deep understanding of software engineering, data workflows, and transparent analysis pipelines. In this work, we present a novel training paradigm developed for undergraduate students in computational and data sciences. Student skills were developed using a framework that integrated core Python programming, data analysis with Pandas, web development with Flask, job queuing with Redis, and containerization with Docker.to promote self-aware thinking and independent problem-solving, we also incorporate narrative reasoning and prompt engineering alongside use of generative AI tools.Grounded in real-world data—specifically, traffic incident reports from the City of Austin—our students build and deploy reproducible data workflows and application programming interfaces (APIs). We discuss the pedagogical strategy, curriculum design, tools used, and alignment with goals from the Science Gateways Community. Results from student feedback and learning assessments suggest increased engagement, skill acquisition, and awareness of reproducibility in scientific computation.