Teaching the unteachable: artificial intelligence enhanced physical digital tools for learning soft robotics

The National Science Foundation Division of Undergraduate Education awarded Kennesaw State University Research And Service Foundation, Inc. $399,953 on October 1, 2026, under the STEM Education program (CFDA 47.076) to develop and implement artificial intelligence–enhanced educational resources for undergraduate soft robotics instruction.

The project will create open-source educational tools combining low-cost 3D-printed hands-on kits, MATLAB-based virtual laboratories, a digital twin platform, and AI-supported instructional modules that deliver personalized feedback. These resources will be integrated into courses spanning introductory engineering through advanced robotics and biomechanics. The project will investigate how students' interactions with physical and digital representations of soft robotic systems influence their understanding of compliance, deformation, and actuation, and how AI-supported learning environments affect engagement, self-efficacy, and learning retention. Effectiveness will be assessed through quasi-experimental research design.

Performance of performance is Kennesaw, Georgia. The period of performance runs from October 1, 2026, through September 30, 2029.

Abstract

This project aims to serve the national interest by improving undergraduate engineering education and preparing the future stem workforce in emerging areas of robotics. Soft robotics is a rapidly growing field with applications in healthcare, manufacturing, wearable technologies, and human-robot interaction, yet educational resources for teaching these concepts remain limited. The significance of this project lies in expanding students? Access to hands-on, technology-enhanced learning experiences that help them understand how flexible materials, compliant mechanisms, and bioinspired robotic systems function. This level 1 engaged student learning project plans to create and study open-source educational resources that combine low-cost 3D-printed hands-on kits, digital twin technologies, and artificial intelligence (AI)-supported learning activities. By integrating these resources into courses ranging from introductory engineering to advanced robotics and biomechanics, the project intends to increase student engagement, strengthen conceptual understanding, and broaden participation in emerging robotics technologies. The goals of the project are to develop, implement, and evaluate educational tools that support learning in soft robotics and compliant mechanisms. The scope includes the creation of portable hands-on kits, matlab-based virtual laboratories, an open-access digital twin platform, and AI-supported instructional tools that provide personalized feedback and guidance. The project plans to investigate how interactions with physical and digital representations of soft robotic systems influence students? Understanding of compliance, deformation, and actuation, and how AI-supported learning environments affect engagement, self-efficacy, and learning retention. The effectiveness of the educational tools will be studied through quasi-experimental research conducted across multiple undergraduate engineering courses using surveys, learning assessments, and classroom implementation data. Findings, instructional materials, software, and design resources will be disseminated through open-source repositories, conference presentations, workshops, and scholarly publications to advance understanding of how physical-digital learning environments and AI-supported instructional practices can improve engineering 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.