Abstract
Chemical engineering education, traditionally grounded in rigorous theoretical instruction and hands-on laboratory experiences, stands on the edge of a transformative shift with the advent of Generative Artificial Intelligence (AI). This manuscript explores the potential of generative AI as a dynamic tool capable of producing innovative content tailored to enhance the learning experience in chemical engineering. This manuscript explores the integration of AI in education and its preliminary applications in creating complex simulations, diverse problem sets, and virtual experiments for chemical engineering students. Generative AI holds immense potential to reshape chemical engineering education, paving the way for a future that not only delivers education, but also dynamically crafts it to suit the unique needs of each learner. This work serves as a roadmap for educators, researchers, and policymakers eager to harness the power of AI to shape the next generation of chemical engineers.
Metadata
Item Type: | Article |
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Creators: | Creators Email / ID Num. Md Zaki, Nurul Asyikin asyikin6760@uitm.edu.my Abd Hashib, Syafiza syafiza0358@uitm.edu.my Ibrahim, Ummi Kalthum ummi985@uitm.edu.my |
Contributors: | Contribution Name Email / ID Num. Chief Editor Ibrahim, Zainuddin macintag@uitm.edu.my |
Subjects: | L Education > LB Theory and practice of education > Educational technology L Education > LB Theory and practice of education > Learning. Learning strategies L Education > LB Theory and practice of education > Educational productivity |
Divisions: | Universiti Teknologi MARA, Kedah > Sg Petani Campus |
Journal or Publication Title: | Journal of Creative Practices in Language Learning and Teaching (CPLT) |
UiTM Journal Collections: | UiTM Journals > Journal of Creative Practices in Language Learning and Teaching (CPLT) |
ISSN: | 1823-464X |
Volume: | 12 |
Number: | 2 |
Page Range: | pp. 17-25 |
Keywords: | Artificial intelligence, chemical engineering, engineering education, generative AI, learning experience |
Date: | 2024 |
URI: | https://ir.uitm.edu.my/id/eprint/114443 |