Reforming Engineering Mechanics Teaching through Generative AI: A Model-Evidence-Verification Approach

Yuan Chang

doi:10.6918/IJOSSER.202611_9(11).0006

Authors

Yuan Chang School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu 215011, China

DOI:

https://doi.org/10.6918/IJOSSER.202611_9(11).0006

Keywords:

Engineering mechanics; generative artificial intelligence; model construction; verification; formative assessment; transfer.

Abstract

An answer to an engineering mechanics problem can read fluently and still rest on the wrong body, support, or boundary condition. The weakness shows up plainly once students turn to generative artificial intelligence (AI), which produces convincing derivations even when the underlying mechanical model is shaky. The teaching design proposed here asks students to commit to their own model first, obtain a critique or a competing interpretation from AI, test the claims against mechanics evidence, revise the model if the evidence demands it, and finally solve a related problem with no help from AI. The checks are ordinary disciplinary tools: free-body diagrams, equilibrium, compatibility, units, limiting cases, and an independent calculation. A truss lesson illustrates the sequence in action when students cannot agree about a roller reaction or about when a zero-force rule applies. The rubric that accompanies the design scores the model, the evidence brought to bear on it, the stated reason for revision, and the transfer performance. The proposal is offered as a workable teaching arrangement and as a starting point for classroom evaluation.

Downloads

Download data is not yet available.

References

[1] Steif, P. S., & Dantzler, J. A. (2005). A statics concept inventory: Development and psychometric analysis. Journal of Engineering Education, 94(4), 363–371. https://doi.org/10.1002/j.2168-9830.2005.tb00864.x.

[2] Steif, P. S., & Dollar, A. (2005). Reinventing the teaching of statics. International Journal of Engineering Education, 21(4), 723–729.

[3] Rosengrant, D., Van Heuvelen, A., & Etkina, E. (2009). Do students use and understand free-body diagrams? Physical Review Special Topics - Physics Education Research, 5, 010108. https://doi.org/10.1103/PhysRevSTPER.5.010108

[4] Savinainen, A., Mäkynen, A., Nieminen, P., & Viiri, J. (2013). Does using a visual-representation tool foster students' ability to identify forces and construct free-body diagrams? Physical Review Special Topics - Physics Education Research, 9, 010104. https://doi.org/10.1103/PhysRevSTPER.9.010104

[5] Aviani, I., Erceg, N., & Mešić, V. (2015). Drawing and using free body diagrams: Why it may be better not to decompose forces. Physical Review Special Topics - Physics Education Research, 11, 020137. https://doi.org/10.1103/PhysRevSTPER.11.020137

[6] Nikolic, S., et al. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559–614. https://doi.org/10.1080/03043797.2023.2213169

[7] Frenkel, M. E., & Emara, H. (2024). ChatGPT-3.5 and -4.0 and mechanical engineering: Examining performance on the FE mechanical engineering and undergraduate exams. Computer Applications in Engineering Education, 32(6), e22781. https://doi.org/10.1002/cae.22781

[8] Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of Engineering Education, 94(1), 103–120. https://doi.org/10.1002/j.2168-9830.2005.tb00832.x

[9] Litzinger, T. A., Lattuca, L. R., Hadgraft, R. G., & Newstetter, W. C. (2011). Engineering education and the development of expertise. Journal of Engineering Education, 100(1), 123–150. https://doi.org/10.1002/j.2168-9830.2011.tb00006.x

[10] Diefes-Dux, H. A., & Salim, W. W. A. W. (2012). Transforming the first-year engineering experience through authentic problem-solving: Taking a models and modeling perspective. Procedia - Social and Behavioral Sciences, 56, 314–332. https://doi.org/10.1016/j.sbspro.2012.09.660

[11] Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871

[12] Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090

[13] VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369

[14] Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

[15] Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535