Collaborative research: an artificial intelligence-based interactive learning support system for improving plan interpretation skills of construction and engineering students

The National Science Foundation Division of Undergraduate Education awarded Florida International University $449,776 on October 1, 2026, under the STEM Education program (CFDA 47.076) to design, develop, and evaluate DAISY, a data-driven artificial intelligence-based interactive learning support system for construction and engineering students. The system will deliver step-by-step guidance, timely feedback, visual learning support, and personalized study aids to help undergraduates develop plan interpretation and spatial reasoning skills—competencies foundational to architecture, engineering, and construction disciplines but commonly difficult for students to master. The project will extend learning support beyond regular class time, connecting information across two- and three-dimensional drawing representations. Florida International University will build an open-access knowledge base containing domain-specific materials, responsible AI use guidance, and a replicable AI-powered teaching framework for construction plan interpretation and estimation. Work is performed in Miami, Florida, through September 30, 2029.

Abstract

This project aims to serve the national interest by advancing artificial intelligence (AI)-supported learning methods that help construction and engineering students develop plan interpretation and spatial reasoning skills. Plan interpretation is fundamental to design, engineering, and construction (AEC) disciplines, yet students often face serious learning difficulties. This level 2 engaged student learning project plans to create a data-drive AI-based interactive learning support system (daisy), which is designed to provide step-by-step guidance, timely feedback, visual learning support, and personalized study aids while reinforcing responsible use of AI. By extending learning support beyond regular class time and helping students connect information across drawings with two- and three-dimensional representations, the project is expected to enhance student engagement and learning experience while providing more effective support for developing plan interpretation and spatial reasoning skills. The significance of the project lies in its potential to improve preparation of construction and engineering students for an AI-augmented workforce, while generating publicly available instructional resources and responsible AI guidance, and evidence-based knowledge about how emerging AI tools can support complex visual-spatial learning in undergraduate stem education. This project intends to: 1) design, develop and evaluate daisy, a novel AI-powered learning system to support undergraduate students in learning essential skills in construction plan interpretation and estimation that are traditionally challenging; 2) build an open-access knowledge base that contains comprehensive domain-specific materials, responsible AI use guidance, and a replicable AI-powered teaching framework for construction plan interpretation; 3) create and validate a suite of assessment tools for measuring both cognitive and affective outcomes of AI-assisted learning; and 4) investigate how personalized AI interventions affect student learning in AEC education and generate transferable knowledge for related disciplines. The project plans to integrate daisy into undergraduate courses and evaluate its effects through performance-based assessments, user interaction data, surveys, and interviews. Research findings, instructional materials, assessment resources, and implementation guidance will be disseminated through open-source repositories, publications, and workshops. The project is expected to advance understanding of how personalized, AI-supported learning can improve visual-spatial reasoning and discipline-specific problem solving in undergraduate stem education. The NSF iuse: EDU program supports research and development projects to improve the effectiveness of stem education for all students. Through the engaged student learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.- subawards are not planned for this award.