Next-Gen Engineering Education with Artificial Intelligence Tutoring

This project aims to serve the national interest by providing effective, personalized learning support that not only improves student learning outcomes but also helps students become more effective lifelong learners with strong Artificial Intelligence (AI) literacy and competencies. While advances in AI have created new opportunities to provide individualized educational assistance at scale, existing AI tools often generate inaccurate information, fail to adapt to students’ evolving understanding, or encourage students to bypass meaningful learning processes. This Level 2 Engaged Student Learning project seeks to develop and evaluate an AI-enhanced tutoring framework that helps students learn more effectively while strengthening their ability to regulate their own learning and use AI responsibly. By integrating advances in AI with evidence-based principles from the learning sciences, the project plans to create an accessible, accurate, and scalable approach to personalized engineering education. The project aligns with the National Science Foundation’s mission and the goals of the IUSE program by advancing undergraduate STEM education, generating new knowledge about effective teaching and learning, and preparing the nation’s future workforce for success in AI-rich professional environments. The resulting framework has the potential to transform how personalized learning support is delivered in engineering and other STEM disciplines. The project plans to develop, implement, and evaluate the Next-Gen Engineering Education with AI Tutoring (NEAT) framework. NEAT integrates an AI tutoring tool that provides accurate, timely, and adaptive feedback, instructional materials designed to promote conceptual understanding, self-regulated learning, and AI literacy, and instructor-facing analytics that provide actionable insights into student learning. The project plans to implement the framework in foundational engineering courses involving students from different majors and instructors with varying levels of teaching experience and evaluate the framework across multiple disciplinary and institutional contexts to study its effectiveness, transferability, and usability. Through the collection of qualitative and quantitative data, the project plans to investigate the effectiveness of the framework in providing personalized and scaffolded learning support, its impact on student learning outcomes, self-regulated learning, and AI literacy, and the factors that influence successful implementation across educational settings. Findings, tools, instructional materials, and implementation resources will be broadly disseminated through publications, conference presentations, workshops, webinars, and a publicly accessible project website. Existing partnerships and professional networks will be leveraged to recruit and engage faculty from community colleges, primarily undergraduate institutions, historically Black colleges and universities (HBCUs), and other institutions to participate in project workshops and webinars. These activities will not only advance understanding of how AI-enhanced personalized learning support can be effectively designed, implemented, and scaled in STEM education, but also support the adoption and adaptation of the NEAT framework across a broad range of institutional settings, extending its impact beyond engineering education and contributing to the broader STEM education community. 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.