Hostname: page-component-6bf8c574d5-rwnhh Total loading time: 0 Render date: 2025-02-16T20:04:27.309Z Has data issue: false hasContentIssue false
  • English
  • Français

Conceptualization of an artificial intelligence-assisted tutoring system for teaching technical drawing skills to undergraduate students

Published online by Cambridge University Press:  16 May 2024

Jonas Fastabend ,
Benedikt Müller ,
Daniel Roth  and
Matthias Kreimeyer
Jonas Fastabend*
Affiliation:
University of Stuttgart, Germany
Benedikt Müller
Affiliation:
University of Stuttgart, Germany
Daniel Roth
Affiliation:
University of Stuttgart, Germany
Matthias Kreimeyer
Affiliation:
University of Stuttgart, Germany
*
[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In design education, technical drawing training requires a large amount of resources. The aim of this paper is to propose a concept for an artificial intelligence-based tutoring system that partly automates technical drawing education. The educational needs of the students are defined via an error analysis of 100 corrected drawing exercises and the definition of 3 error clusters with 134 different error types. Three sub-concepts with a collection of training exercises are proposed for the tutoring system to mitigate these errors. The resulting concept is validated by a survey with 29 students.

Keywords

design educationartificial intelligence (AI)design skillscomputational design methodstechnical drawings
Type
Design Education
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2835 - 2844
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Barr, R.E. (2004), “The current status of graphical communication in engineering education”, in Expanding educational opportunities through partnerships and distance learning ; 34th annual frontiers in education; FIE 2004; the Hyatt Regency Savannah, Savannah, Georgia, October 20–23, 2004; conference proceedings; [34th ASEE/IEEE Frontiers in Education Conference], Oct. 20–23, 2004, Savannah, GA, USA, IEEE Operations Center, Piscataway, NJ, pp. 975–980. https://doi.org/10.1109/FIE.2004.1408688CrossRefGoogle Scholar
Bednarz, R.S. and Lee, J. (2011), “The components of spatial thinking: empirical evidence”, Procedia-Social and Behavioral Sciences, Vol. 21, pp. 103–107. https://doi.org/10.1016/j.sbspro.2011.07.048CrossRefGoogle Scholar
Biggs, J. B.; Tang, C. (2011): Teaching for quality learning at university. What the student does. 4th edition. Maidenhead: McGraw-Hill/Society for Research into Higher Education/Open University Press (SRHE and Open University Press Imprint). Available online at https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=405333Google Scholar
Blockeel, H. and Vanschoren, J. (2007), “Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning”, in Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D. and Skowron, A. (Eds.), Knowledge discovery in databases: PKDD 2007: 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17–21, 2007 ; proceedings, Lecture notes in computer science Lecture notes in artificial intelligence, Vol. 4702, Springer, Berlin, Heidelberg, pp. 6–17. https://doi.org/10.1007/978-3-540-74976-9_5Google Scholar
Garland, N.P., Glitho, R. and Wade, R. (2017), “THE CHALLENGES FACING EDUCATION IN ENGINEERING DRAWING PRACTICE”, in Building community: Design education for a sustainable future proceedings of the 19th International Conference on Engineering and Product Design Education, HiOA University, Oslo, Norway, on the 7th & 8th September 2017, Institution of Engineering Designers, Westbury, Wiltshire, United Kingdom, pp. 254–259.Google Scholar
Hauptman, H. (2010), “Enhancement of spatial thinking with Virtual Spaces 1.0”, Computers & Education, Vol. 54 No. 1, pp. 123–135. https://doi.org/10.1016/j.compedu.2009.07.013CrossRefGoogle Scholar
Idowu, S., Osman, O., Strüber, D. and Berger, T. (2022), “On the effectiveness of machine learning experiment management tools”, in Harman, M. and Miller, H. (Eds.), Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice, 21 05 2022 29 05 2022, Pittsburgh Pennsylvania, ACM, New York, NY, USA, pp. 207–208. https://doi.org/10.1145/3510457.3513084Google Scholar
Kornkasem, S. and Black, J.B. (2015), “Formation of spatial thinking skills through different training methods”, Cognitive processing, 16 Suppl 1, pp. 281–285. https://doi.org/10.1007/s10339-015-0707-8CrossRefGoogle ScholarPubMed
Kosse, V. (2005), “Engineering Drawing as a Global Language for Engineers”, in Radcliffe, D and Humphries, J (Eds.), Proceedings of the 4th ASEE/AaeE Global Colloquium on Engineering Education: Globalisation of Engineering Education, Kindergarten to Year 12 Pipeline, Transformation of the Disciplines, University of Queensland, CD Rom, pp. 1–9.Google Scholar
Labisch, S. and Wählisch, G. (2020), Technisches Zeichnen, Springer Fachmedien Wiesbaden, Wiesbaden. https://doi.org/10.1007/978-3-658-30650-2CrossRefGoogle Scholar
Leake, J.M. and Goldstein, M.H. (2022), Engineering design graphics: Sketching, modeling, and visualization, Third edition, Wiley, Hoboken, NJ.Google Scholar
Lille, H.T. (2013), “Engineering drawing as a visual channel in communication medium”, Agronomy research, Vol. 11, pp. 249–254.Google Scholar
Moreno-García, C.F., Elyan, E. and Jayne, C. (2019), “New trends on digitisation of complex engineering drawings”, Neural Computing and Applications, Vol. 31 No. 6, pp. 1695–1712. https://doi.org/10.1007/s00521-018-3583-1CrossRefGoogle Scholar
Münzer, S. and Zadeh, M.V. (2016), “Acquisition of spatial knowledge through self-directed interaction with a virtual model of a multi-level building: Effects of training and individual differences”, Computers in Human Behavior, Vol. 64, pp. 191–205. https://doi.org/10.1016/j.chb.2016.06.047CrossRefGoogle Scholar
Rahmandad, H., Repenning, N.P. and Sterman, J.D. (2009), “Effects of feedback delay on learning”, System Dynamics Review, Vol. 25, pp. 309–338. https://doi.org/10.1002/sdr.427CrossRefGoogle Scholar
Sampaio, A.Z. (2018), “Historical Evolution of Technical Drawing in Engineering”, in Changing higher education, one teacher at a time: Third International Conference of the Portuguese Society for Engineering Education Aveiro, June 27–29, 2018, 6/27/2018 - 6/29/2018, Aveiro, IEEE, Piscataway, NJ, pp. 1–9. https://doi.org/10.1109/CISPEE.2018.8593496Google Scholar
Seff, A., Ovadia, Y., Zhou, W. and Adams, R.P. (2020), “SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design”, ArXiv, abs/2007.08506.Google Scholar
Sharma, P. and Fiedler, S. (2007), “Supporting self-organized learning with personal Webpublishing technologies and practices”, Journal of Computing in Higher Education, Vol. 18 No. 2, pp. 3–24. https://doi.org/10.1007/BF03033411CrossRefGoogle Scholar