Abstract:
This literature review investigates the influence of Chat GPT AI on the effectiveness of the civil engineering curriculum and student performance. The study explores the use of Chat GPT AI in education and emphasizes the significance of rubric assessment in evaluating student achievements. The findings reveal that incorporating Chat GPT AI can significantly enhance the learning process by providing prompt and accurate responses to students' queries. However, the study emphasizes the continued importance of human interaction in the assessment process, as rubric assessment remains crucial for evaluating student performance and fostering motivation for better outcomes. The implications underscore the need to align the curriculum with industry standards and leverage technology to enrich the learning experience. These findings hold potential for educators and policymakers seeking to enhance educational quality and produce highly qualified civil engineering graduates.
Highlights:
- Chat GPT AI: Improving Efficiency - The study explores the impact of Chat GPT AI in civil engineering education, highlighting its potential to enhance the efficiency of the learning process by providing quick and accurate answers to students' questions.
- Importance of Rubric Assessment - The research emphasizes the crucial role of rubric assessment in measuring student performance and motivating them to achieve better results, highlighting its significance alongside the integration of technology.
- Adapting Curriculum and Utilizing Technology - The study highlights the importance of adapting the curriculum to meet industry needs and standards, while also leveraging technology, such as Chat GPT AI, to enhance the learning experience and improve the quality of civil engineering education.
Keywords: Chat GPT AI, civil engineering education, curriculum efficiency, student performance, rubric assessment.
References
M. H. Jarrahi, D. Askay, A. Eshraghi, and P. Smith, “Artificial intelligence and knowledge management: A partnership between human and AI,” Bus. Horiz., vol. 66, no. 1, pp. 87–99, 2023, doi: https://doi.org/10.1016/j.bushor.2022.03.002.
E. L. Hill-Yardin, M. R. Hutchinson, R. Laycock, and S. J. Spencer, “A Chat(GPT) about the future of scientific publishing.,” Brain. Behav. Immun., vol. 110, pp. 152–154, May 2023, doi: 10.1016/j.bbi.2023.02.022.
T. Penyusun, Panduan Penyusunan Kurikulum Pendidikan Tinggi. 2020.
S. Suwarno and C. Aeni, “Pentingnya Rubrik Penilaian Dalam Pengukuran Kejujuran Peserta Didik,” Edukasi J. Pendidik., vol. 19, no. 1, p. 161, 2021, doi: 10.31571/edukasi.v19i1.2364.
R. Winanda and D. Ham, “Analisis Kesiapan Sumber Daya Manusia Pada Kontraktor Di Surabaya Menghadapi Masyarakat Ekonomi Asean,” Pratama Tek. Sipil, pp. 1–8, 2015, [Online]. Available: http://studentjournal.petra.ac.id/index.php/teknik-sipil/article/view/3885
R. Panisha, “Pengembangan Kurikulum Berbasis Luaran Di Era Disrupsi (Outcomes-Based Curriculume In Disrupstion Era - OBCiDE) Berdasarkan Stakholders Internal Di Program Studi S1 Teknik Sipil FT UNY,” J. Pendidik. Tek. Sipil, vol. 4, no. 2, pp. 168–176, 2023, doi: 10.21831/jpts.v4i2.53932.
M. Suryaman, “Orientasi Pengembangan Kurikulum Merdeka Belajar,” pp. 13–28, 2020.
A. R. H. Bisjoe, “Menjaring Data dan informasi penelitian melalui Focus Group Discussion(FGD): Belajar dari Praktek Lapangan,” Info Tek. EBONI, vol. 15, no. 1, pp. 17–28, 2018, [Online]. Available: http://ejournal.forda-mof.org/ejournal-litbang/index.php/buleboni/article/view/5117
M. Waluyati, “Penerapan Fokus Group Discussian (FGD) Untuk Meningkatkan Kemampuan Memanfaatkan Lingkungan Sebagai Sumber Belajar,” J. Edutech Undiksha, vol. 8, no. 1, p. 80, 2020, doi: 10.23887/jeu.v8i1.27089.
International Engineering Alliance, “IEA Graduate Attributes and Professional Competency Profiles Graduate Attributes and Professional Competencies Executive Summary,” pp. 1–16, 2013, [Online]. Available: http://www.ieagreements.org.
Samiaji Sarosa, Analisis Data Penelitian Kualitatif. 2021.