Abstract:

The rapid emergence of AI-assisted tools and large language models (LLMs) is transforming how students learn, design, and solve problems in engineering education. While these technologies can support analysis, simulation, and rapid prototyping, concerns remain about their impact on deep learning, critical thinking, and practical skill development. This paper presents a structured approach to integrating AI-assisted tools with hands-on learning in undergraduate engineering education, using three courses at Bucknell University as case studies.

In a first-year interdisciplinary design course, students use AI-assisted platforms to analyze real-world solar energy systems while engaging in field data collection, system design, and economic evaluation. In a sophomore-level digital systems course, students interact with AI systems to support research and verification while designing, building, and testing logic circuits using physical hardware. In an upper-level electrical engineering course, students engage with real power system infrastructure, including substations, solar arrays, and grid-connected equipment, combining field observations, system analysis, and data interpretation with AI-supported tools.

Results show that students develop stronger conceptual understanding, problem-solving ability, and confidence when hands-on activities are intentionally combined with AI-assisted tools rather than replaced by them. Across all three levels, students interacting with AI systems were better able to validate outputs, identify errors, and apply engineering judgment when grounded in physical experimentation and real-world system exposure.

This study extends prior engineering education work by reframing hands-on learning within the broader context of AI-driven educational transformation. It highlights the importance of maintaining experiential learning in the age of artificial intelligence and proposes a balanced educational framework where AI acts as a support tool rather than a substitute for learning. The findings contribute to ongoing discussions on the role of AI in higher education and provide practical guidance for designing technology-enhanced learning environments that preserve depth, engagement, and authenticity.

Keywords:

Engineering education, hands-on learning, AI in education, experiential learning, STEM education, technology-enhanced learning.