Identifying essential competencies for artificial intelligence assisted programming in foundational engineering courses

The National Science Foundation Division of Undergraduate Education awarded the University of Louisville $295,233 on January 1, 2027, under the STEM Education program (CFDA 47.076) to identify essential competencies for AI-assisted programming in foundational engineering courses.

Over two years through December 31, 2028, the project will develop a framework of updated learning objectives for programming instruction in an era of widespread AI integration. The recipient will characterize current AI-assisted programming practices among subject matter experts, prioritize foundational competencies through consensus-building methods, and translate competencies into measurable learning objectives. Phase 1 involves interviews with experts and thematic analysis to generate an essential competencies list. Phase 2 applies Q-sorting to rank competencies and the Delphi method to build consensus across divergent expert viewpoints. Phase 3 translates the validated competency framework into measurable learning objectives. The project addresses a misalignment between traditional programming pedagogy and workforce demands, offering engineering educators evidence-based guidance to determine which traditional skills remain essential, which require reduced emphasis, and which new competencies should be introduced so students can leverage AI safely and effectively. Work is performed in Louisville, Kentucky.

Abstract

This project aims to serve the national interest by developing a framework of updated learning objectives for programming in foundational engineering courses in an ERA of widespread, accessible, artificial intelligence (AI) technologies. The rapid integration of AI into professional software development tools and workflows has created a misalignment between traditional programming pedagogy and the competencies the engineering workforce now demands. A consensus about how to update instruction has not yet formed. Over the course of two years, this engaged student learning level 1 project will identify the essential, foundational competencies for AI-assisted programming and translate them into learning objectives, determining which traditional skills remain essential, which require reduced emphasis, and which new competencies should be introduced so that students and graduates can leverage AI safely and effectively. The significance of this project lies in identifying what students need to learn to be able to program with AI, offering engineering educators timely, evidence-based guidance to align instruction with professional practice. The goals of this project are to (1) characterize current AI-assisted programming practices, (2) prioritize the competencies that matter most for foundational learning, and (3) translate the competencies into measurable learning objectives. In phase 1, interviews will be conducted with subject matter experts, and thematic analysis will be used to generate a list of essential competencies. In phase 2, experts will be asked to participate in a q-sorting study to rank these competencies, and analysis will characterize divergent viewpoints. The delphi method will then be used with a panel of diverse experts to build consensus across perspectives, yielding a final prioritized and validated set of competencies. In phase 3, measurable learning objectives for programming in foundational engineering courses will be developed utilizing backwards design practices and bloom's revised taxonomy. Following assessment by an external evaluator and an advisory board, dissemination efforts are planned to share procedures and findings in a wide range of publications, workshops, and conferences. Thus, the resulting competency framework has the potential to align classroom learning with modern programming workflows, support student engagement and workforce readiness, and broaden participation in computing and other stem fields on a national scale. 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.