Student vs Machine: Comparing Artificial Neural Network Predictions with Student Estimates of Market Price Using Function Structure Models

DS 116: Proceedings of the DESIGN2022 17th International Design Conference

Year: 2022
Editor: Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović
Author: Apurva Rajesh Patel, Joshua D. Summers
Series: DESIGN
Institution: The University of Texas at Dallas, United States of America
Section: Artificial Intelligence and Data-Driven Design
Page(s): 1669-1678
DOI number: https://doi.org/10.1017/pds.2022.169
ISSN: 2732-527X (Online)

Abstract

This paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research.

Keywords: artificial intelligence (AI), functional modelling, design education