2025 ASEE Annual Conference & Exposition

This paper introduces an aeroelasticity lab activity designed for sophomore Aerospace engineering students, combining aerodynamics and structural mechanics. The lab aims to bridge the gap between theoretical knowledge and real-world applications by guiding students through industry-relevant problems, offering hands-on experience in model creation, data acquisition, and validation. Students are tasked with creating aerodynamic loading and structural deformation models for an aircraft wing and its spar beam. Using a low-speed wind tunnel, they collect data to compare against their models, fostering essential industry skills like model validation, experimental testing, and comparison between the two—skills difficult to impart in a traditional lecture setting.

To further enhance this experiential learning activity, we have incorporated AI-based tools into the lab, specifically chatbot generated solutions for the lab’s core problems. In a novel extension of the assignment, students critically evaluate the AI-generated solutions by comparing them to their own analytical work, identifying where and why the AI's solutions may be correct or incorrect. This AI-assisted comparative analysis encourages deeper engagement with both AI outputs and traditional engineering methods, fostering critical evaluation and explanation skills.

This integration of AI generated solutions into the lab is designed to expose students to the strengths and limitations of AI tools, preparing them to interact with these technologies as they become more prevalent in engineering practice. Students inherently learn to recognize areas where AI excels, as well as areas where human expertise remains critical. By maintaining existing lab infrastructure while updating the assignments with AI tools, the lab remains both modern and adaptable to future technological advancements. Additionally, this framework enables instructors to keep assignments relevant year after year, providing a flexible architecture for future iterations.

A key objective of this adaptation is to prepare students for a future where AI-generated solutions may surpass even the best student work. However, the skill that remains irreplaceable is the ability to critically assess the correctness of solutions—whether human or AI-generated. This paper presents findings on student impressions of this modern adaptation of comparative analysis, focusing on how the integration of AI affects their engagement, understanding, and preferences compared to traditional lab assessments.
In conclusion, the introduction of AI tools into the aeroelasticity lab provides an educational experience that bridges the gap between traditional engineering practices and the rapidly advancing field of AI. This approach offers valuable insights into the evolving role of AI in engineering education while ensuring that students develop the critical skills necessary for the future workforce.