SCIPE: Chishiki.ai: A sustainable, diverse, and integrated CIP community for Artificial Intelligence in Civil and Environmental Engineering

Chishiki.ai is an integrated community of CI professionals (CIPs) across artificial intelligence (AI) and civil and environmental engineering (CEE) practices to bolster U.S. infrastructure, aligning with thrust areas identified by the 2020 National Artificial Intelligence Initiative Act and the 2022 Infrastructure Investment and Jobs Act. THe Chishiki project adopts four strategies to build a sustainable, diverse, and integrated community of CI professionals for AI in CEE by (1) fostering collaboration between CIPs and domain experts through initiatives such as research summits, graduate and undergraduate fellowships, joint research initiatives, and industrial partnerships; (2) offering personalized and scalable learning environments powered by AI; (3) developing innovative AI-enabled CI architectures for reproducible and efficient workflows; and (4) creating a diverse, sustainable CIP community through engagement with historically underrepresented institutions through recruitment and research initiatives.

Chishiki offers peer mentoring support and works with the NSF ACCESS Computational Science Support Network (CSSN) to support CI professionals in research activities related to CI and CEE. The integration of CIPs into CEE research is enabled through an active community of practice, providing opportunities for professional development, collaboration, and well-being.

The project will publish best practices on partnerships, broadening adoption, and democratizing access to CI solutions in CEE. Chishiki offers AI-enhanced CI solutions and supports an integrated and diverse CIP community dedicated to transforming Civil and Environmental Engineering.

Through Chishiki.ai, the project develops new courses for CI professionals to build and support sophisticated CI frameworks that foster AI-driven research innovations. The courses on AI4CI and CI4AI cover AI-enabled programming, AI-enhanced performance tuning of High-Performance Computing (HPC) systems, AI-driven knowledge discovery and curation, and building large-scale production-ready AI systems.

The course on Scientific Machine Learning explores techniques for explainable AI, differentiable programming, and uncertainty propagation, thus enabling CI professionals to understand the need and use of AI in CEE. The Chishiki project develops a novel, scalable learning environment by building context-aware Large Language Models through reinforcement learning to generate personalized quizzes and explanations.

The personalized AI tutor facilitates generating individualized quizzes and customized explanations to suit the individual's needs and learning abilities. The scalable and personalized AI tutor-powered courses will be available as open-access content on the Cornell Virtual Workshop (CVW) learning platform, reaching a broad community of CIPs. To accelerate AI-enhanced research, the project supports the development of sophisticated AI surrogates based on graph neural networks and differentiable simulations for optimization and engineering design, develops frameworks to deploy foundational AI models on memory-limited edge devices for structural health monitoring and transportation planning, and HPC systems to develop exemplar applications of AI-enabled CEE.

The Chishiki project also supports AI-assisted code development to accelerate scientific research. The project's deliverables will be available on existing NSF-funded platforms, DesignSafe and the Texas Advanced Computing Center (TACC), broadening the adoption and integration of AI-enhanced CI innovations. The developments will be publicly accessible as open-course content and open-source solutions for broader dissemination.

The project goal is to benefit more than 500 CIPs nationwide and to train more than 300,000 users worldwide through this personalized and scalable learning platform.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.