2026 ASEE Annual Conference & Exposition

The Impact of Real-Time Additive Manufacturing Feedback and Design Cognition Style on Student CAD Performance

Presented at Mechanical Engineering (MECH) Session 6: Mechanical Design, CAD, and Manufacturing

This Work in Progress study investigates how real time AI generated Additive Manufacturing print time feedback influences student design behavior and how individual design cognition styles interact with this feedback. Students will participate in four CAD-based activities. The AI tool’s functionality is tested across four interaction modes: baseline (no AI tool), passive (always visible), voluntary (hidden until clicked), and forced (automatic pop-up with a 2 second design pause). To address the relationship between cognitive style and creativity, participants complete the Cognitive Style Index (CSI). The CSI categorizes problem solvers on a spectrum from Analytic (structured, logical, and incremental improvement) to Intuitive (holistic and immediate sensing of solution). Creative performance is evaluated via a CAD based Alternative Uses Test (AUT) involving furniture assembly. While AUTs typically focus on divergent thinking, this study adapts it to engineering by measuring "uniqueness" through structural characteristics and functional constraints. This research aims to inform the development of adaptive educational tools that cater to diverse cognitive styles, ensuring AI feedback serves as an aid instead of a distraction.

Authors

Mr. Leith Ronin Dessureault Florida Polytechnic University
Dr. Apurva Patel http://orcid.org/0009-0002-5860-5267 Florida Polytechnic University [biography]

Apurva Patel is an Assistant Professor of Mechanical Engineering at Florida Polytechnic University. He earned his BS in Mechanical Engineering from Clemson University. He also completed his graduate studies at Clemson University, earning his MS in Mechanical Engineering (modeling behavior in function structures) in May 2018, and his PhD in Mechanical Engineering (individual differences in function modeling) in August 2021. Apurva’s research interests include human behavior in engineering design (system modeling and interpretation, influence of individual differences, and collaboration in engineering teams), manufacturing automation and assembly behaviors, graph-based complexity analysis of products and systems, and prediction using artificial neural networks. In particular, his current explorations are focused on modeling student design approach using machine learning, and understanding the effects of AI integration in the design process on human designers.

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