Research on an AI-Empowered Three-Stage Closed-Loop Teaching Model for Chemical Engineering Practical Training

Jun Yang; Yixin Zhang; Jingjing Xie; Yuqin Chen; Chaoting Fu

doi:10.6918/IJOSSER.202607_9(7).0009

Authors

Jun Yang Tianfu New Energy and New Materials Industry College, Meishan Vocational and Technical College, Meishan 620010, China
Yixin Zhang School of Emergency Technology and Management, University of Emergency Management, Langfang 065201, Hebei Province, China
Jingjing Xie School of Emergency Technology and Management, University of Emergency Management, Langfang 065201, Hebei Province, China
Yuqin Chen Tianfu New Energy and New Materials Industry College, Meishan Vocational and Technical College, Meishan 620010, China
Chaoting Fu Tianfu New Energy and New Materials Industry College, Meishan Vocational and Technical College, Meishan 620010, China

DOI:

https://doi.org/10.6918/IJOSSER.202607_9(7).0009

Keywords:

Artificial intelligence; chemical engineering practical training; pre-job training; three-stage closed loop; process assessment.

Abstract

To address insufficient pre-class preparation, limited individualized guidance, inadequate fault-analysis training, and delayed report feedback in higher vocational chemical engineering practice, this study develops a three-stage closed-loop teaching model consisting of intelligent pre-training, scenario-based in-class guidance, and post-training feedback and evaluation. Taking the course Pre-job Training for Chemical Process Operators as the carrier, generative artificial intelligence is used for knowledge diagnosis, process-flow recognition, scenario simulation, data checking, and personalized feedback. Equipment manuals, operating procedures, safety regulations, fault cases, and assessment rubrics form the course knowledge resources, while teacher-defined rules establish the safety boundary of AI use. The model was applied to a class of 43 students, of whom 39 had complete process records. The complete-sample mean score was 80.09, the median was 79.00, the pass rate was 100%, and 46.15% of students scored 80 or above. The model links preparation, on-site operation, and post-training reflection, offering a practical approach to differentiated guidance, process-oriented assessment, and vocational competence development.

Downloads

Download data is not yet available.

References

[1] Ogunleye, B., Zakariyyah, K. I., Ajao, O., et al. (2024). A systematic review of generative AI for teaching and learning practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636

[2] Ali, D., Fatemi, Y., Boskabadi, E., et al. (2024). ChatGPT in teaching and learning: A systematic review. Education Sciences, 14(6), 643. https://doi.org/10.3390/educsci14060643

[3] Sun, D., Boudouaia, A., Zhu, C., et al. (2024). Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. International Journal of Educational Technology in Higher Education, 21, 14. https://doi.org/10.1186/s41239-024-00456-2

[4] Farrokhnia, M., Banihashem, S. K., Noroozi, O., et al. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14794408.2023.2297846

[5] Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14794408.2023.2218001

[6] 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. https://doi.org/10.1016/j.lindif.2023.102274

[7] Tlili, A., Shehata, B., Adarkwah, M. A., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-7

[8] Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410

[9] Chiu, T. K. F., Xia, Q., Zhou, X., et al. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2023.100118

[10] Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-1

[11] Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, 38. https://doi.org/10.1186/s41239-023-00410-2

[12] Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692

[13] Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO.

[14] Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12547

[15] De Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305–308. https://doi.org/10.1126/science.1230582