The Integration of Generative AI into a 4-Step Model for Teaching Descriptive Geometry in Higher Education

DOI

10.2991/978-94-6239-823-8_20How to use a DOI?

Keywords

descriptive geometry; 4-step model; generative AI

Abstract

This study examines the integration of generative artificial intelligence into higher education, specifically in descriptive geometry education with special attention on the development of spatial reasoning skills. The research presents an innovative four-step pedagogical model based on diagnostic assessment, adaptive curriculum design, collaborative learning, and continuous curriculum development. The model was tested among first-year engineering students at the University of Miskolc. The study suggests that the analysis of errors in diagrams generated by generative AI, combined with collaborative group work, significantly improved students’ performance by the end of the semester. The results indicate that generative artificial intelligence tools can effectively support visualization, the identification of geometric errors, and the development of critical thinking skills. Students’ performance showed considerable improvement because of these targeted interventions. Based on these findings, it can be concluded that generative artificial intelligence may play an important role in the methodological renewal of future engineering education.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Erika Rozgonyi
PY  - 2026
DA  - 2026/11/04
TI  - The Integration of Generative AI into a 4-Step Model for Teaching Descriptive Geometry in Higher Education
BT  - Proceedings of the Innovation in Engineering Education 2026 Conference (IEE 2026)
PB  - Atlantis Press
SP  - 233
EP  - 247
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-823-8_20
DO  - 10.2991/978-94-6239-823-8_20
ID  - Rozgonyi2026
ER  -