Application of Modern Digital Technologies and Artificial Intelligence Tools in Teaching Engineering Graphics at Pedagogical Universities

Authors

  • Khalimov Mokhir Karimovich Uzbekistan National Pedagogical University named after Nizami, Head of the Department of Fine Arts and Engineering Graphics, Associate Professor, PhD, Uzbekistan

DOI:

https://doi.org/10.55640/eijp-06-04-03

Keywords:

Generative, intelligence, competency

Abstract

This study examines the integration of artificial intelligence and modern digital technologies into engineering graphics education at pedagogical universities. The research evaluates the effectiveness of AI-enhanced learning environments in developing spatial visualization skills, technical competencies, and pedagogical readiness among pre-service teachers. Through a comprehensive mixed-methods analysis, the study demonstrates that thoughtful integration of intelligent tutoring systems, generative AI platforms, virtual reality applications, and advanced CAD tools significantly improves student learning outcomes compared to traditional instruction.

References

Leopold, C., Górska, R.A., & Sorby, S.A. (2019). International experiences in developing spatial visualization abilities of engineering students. Journal for Geometry and Graphics, 23(1), 123-137.

Zawacki-Richter, O., Marín, V.I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39.

Marunic, G., & Glazar, V. (2013). Spatial ability through engineering graphics education. International Journal of Technology and Design Education, 23(3), 703-715.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.

Kasneci, E., Seßler, K., Küchemann, S., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

Company, P., Contero, M., Otey, J., & Plumed, R. (2015). Approach for developing coordinated rubrics to convey quality criteria in CSCL environments. Computer-Aided Design, 63, 101-117.

Martín-Gutiérrez, J., Mora, C.E., Añorbe-Díaz, B., & González-Marrero, A. (2017). Virtual technologies trends in education. EURASIA Journal of Mathematics, Science and Technology Education, 13(2), 469-486.

Siemens, G., & Baker, R.S.J.d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254.

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Cuban, L. (2001). Oversold and underused: Computers in the classroom. Harvard University Press.

Sorby, S.A. (2009). Educational research in developing 3‐D spatial skills for engineering students. International Journal of Science Education, 31(3), 459-480.

Uttal, D.H., Meadow, N.G., Tipton, E., et al. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139(2), 352-402.

Wai, J., Lubinski, D., & Benbow, C.P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge. Journal of Educational Psychology, 101(4), 817-835.

Hegarty, M. (2014). Spatial thinking in undergraduate science education. Spatial Cognition & Computation, 14(2), 142-167.

Miller, C.L. (1992). Enhancing visual literacy of engineering students through the use of real and computer generated models. Engineering Design Graphics Journal, 56(1), 27-38.

Contero, M., Naya, F., Company, P., Saorin, J.L., & Conesa, J. (2005). Improving visualization skills in engineering education. IEEE Computer Graphics and Applications, 25(5), 24-31.

Paivio, A. (2013). Imagery and verbal processes. Psychology Press.

Piaget, J., & Inhelder, B. (1971). Mental imagery in the child. Basic Books.

Barr, R.E. (2004). The current status of graphical communication in engineering education. Proceedings of the 59th ASEE Annual Conference & Exposition.

Lieu, D.K., & Sorby, S.A. (2009). Visualization, modeling, and graphics for engineering design. Delmar Cengage Learning.

Basham, K.L., & Uziak, J. (2017). Enhancing spatial visualization skills in engineering drawing course. European Journal of Engineering Education, 42(2), 252-266.

Garmendia, M., Guisasola, J., & Sierra, E. (2014). First-year engineering students' difficulties in visualization and drawing tasks. European Journal of Engineering Education, 39(1), 15-32.

Branoff, T.J., & Dobelis, M. (2012). The relationship between spatial visualization ability and students' ability to model 3D objects from engineering assembly drawings. Engineering Design Graphics Journal, 76(3), 37-43.

Field, B.W. (2004). A course in spatial visualization. Journal for Geometry and Graphics, 8(2), 201-209.

Bacca, J., Baldiris, S., Fabregat, R., Graf, S., & Kinshuk. (2014). Augmented reality trends in education: A systematic review. Educational Technology & Society, 17(4), 133-149.

Kaufmann, H., & Schmalstieg, D. (2003). Mathematics and geometry education with collaborative augmented reality. Computers & Graphics, 27(3), 339-345.

Radianti, J., Majchrzak, T.A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education. Computers & Education, 147, 103778.

Kulik, J.A., & Fletcher, J.D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42-78.

Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning. Journal of Educational Psychology, 106(2), 331-347.

Graesser, A.C., & McNamara, D.S. (2010). Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45(4), 234-244.

OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.

Qadir, J. (2023). Engineering education in the era of ChatGPT. arXiv preprint arXiv:2302.10337.

Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1-10.

Mollick, E.R., & Mollick, L. (2023). New modes of learning enabled by AI chatbots. Available at SSRN 4300783.

Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 427-436.

Wang, Z., Simoncelli, E.P., & Bovik, A.C. (2003). Multiscale structural similarity for image quality assessment. Proceedings of the 37th Asilomar Conference on Signals, Systems & Computers, 2, 1398-1402.

Conati, C., Barral, O., Putnam, V., & Rieger, L. (2021). Toward personalized XAI: A case study in intelligent tutoring systems. Artificial Intelligence, 298, 103503.

Papamitsiou, Z., & Economides, A.A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49-64.

Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317.

Mishra, P., & Koehler, M.J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.

Ertmer, P.A., & Ottenbreit-Leftwich, A.T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255-284.

Abdug‘aniev, N.A. (2020). Muhandislik grafikasi fanini o‘qitishda innovatsion texnologiyalarning o‘rni. O‘zbekiston Milliy universiteti Ilmiy axborotlari, 1(4), 234-239.

Nurmatov, Sh.S., & Xolmatova, Z.T. (2022). Pedagogika universitetlarida kompyuter grafikasi va CAD tizimlarini o‘qitish metodikasi. Toshkent: O‘qituvchi nashriyoti.

Maxmudov, R.M., & Karimov, A.A. (2019). Chizma geometriya va muhandislik grafikasi o‘qitish metodikasi. Toshkent: Fan nashriyoti.

Yo‘ldoshev, J.G‘. (2021). Ta’limda sun’iy intellekt texnologiyalari: O‘zbekiston tajribasi va xorijiy amaliyot. Zamonaviy ta’lim, 5(98), 12-18.

Ismoilov, M.I., & Rahmonov, B.X. (2020). Bo‘lajak muhandislik o‘qituvchilarining kasbiy tayyorgarligini shakllantirish. Pedagogik ta’lim, 4(3), 67-73.

Tursunov, I.T. (2021). Fazoviy tasavvurni rivojlantirishda kompyuter grafikasining ahamiyati. Fan va texnologiya, 3(4), 112-118.

Downloads

Published

2026-04-08

How to Cite

Khalimov Mokhir Karimovich. (2026). Application of Modern Digital Technologies and Artificial Intelligence Tools in Teaching Engineering Graphics at Pedagogical Universities. European International Journal of Pedagogics, 6(04), 15–22. https://doi.org/10.55640/eijp-06-04-03