From Traditional Engineering to AI Driven Energy Systems: Professional Identity Transformation among Oil and Gas Engineering Students

Chenyu Wu; Zhiyu Xue; Shenghao Zhang; Bo Gao; Naixin Hou; Gaoshan Wu

doi:10.62177/chst.v3i3.1649

Authors

Chenyu Wu Xi’an Shiyou University
Zhiyu Xue Macau University of Science and Technology (MUST)
Shenghao Zhang City University of Hong Kong (Dongguan)
Bo Gao Xi’an Shiyou University
Naixin Hou Xi’an Shiyou University
Gaoshan Wu Macau University of Science and Technology (MUST)

DOI:

https://doi.org/10.62177/chst.v3i3.1649

Keywords:

AI-Enabled Energy Systems, Professional Identity, Competency Awareness, Oil and Gas Engineering, Thematic

Abstract

This research paper aims to analyze the perception of oil and gas engineering students about the transformation of their engineering education into artificial intelligence-enabled energy systems and the influence of this change on the development of professional identity, competency awareness, and career prospects of students. The findings are based on the analysis of semi-structured interviews with 12 participants and show that AI is considered an additional element to existing knowledge, which forms hybrid professional identity but generates competency anxiety.

Downloads

Download data is not yet available.

References

Erhueh, O. V., Nwakile, C., Akano, O. A., Esiri, A. E., & Hanson, E. (2024). Digital transformation in energy asset management: Lessons for building the future of energy infrastructure. Global Journal of Research in Science and Technology, 2(02), 010-037.

Ma, J. (2023). The application of artificial intelligence technology in the safety monitoring system of oil and gas ground. Procedia Computer Science, 228, 486-493.

Davidsson, P., & Sufyan, M. (2023). What does AI think of AI as an external enabler (EE) of entrepreneurship? An assessment through and of the EE framework. Journal of Business Venturing Insights, 20, e00413.

Díaz Palencia, J. L. (2025). Institutional distance between academia and industry in relation to the pedagogy in a fluid mechanics course. Industry and Higher Education, 39(4), 472-481.

Afridi, Y. S., Ahmad, K., & Hassan, L. (2022). Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions. International Journal of Energy Research, 46(15), 21619-21642.

Al-Hajri, A., Hamouda, A. M., & Abdella, G. M. (2025). Sustainability-based strategic framework for digital transformation in the oil and gas industry. IEEE Access.

Sarraf‐Yazdi, S., Pisupati, A., Goh, C. K., Ong, Y. T., Toh, Y. R., Goh, S. P. L., & Krishna, L. K. R. (2024). A scoping review and theory‐informed conceptual model of professional identity formation in medical education. Medical Education, 58(10), 1151-1165.

Wu, J., & Smith, K. N. (2026). Shaping Possible Selves: The Influence of Internships on Women's Career Paths in Computing. American Educational Research Journal, 00028312261423533.

Beane, M., & Anthony, C. (2024). Inverted apprenticeship: How senior occupational members develop practical expertise and preserve their position when new technologies arrive. Organization Science, 35(2), 405-431.

Ahn, Y. H., & Oh, E. Y. (2024). Effects of the international training program for enhancing intelligent capabilities through blended learning on computational thinking, artificial intelligence competencies, and core competencies for the future society in graduate students. Applied Sciences, 14(3), 991.

Hu, Z., He, H., Zhang, C., & Guan, Y. (2025). Development and influencing factors of artificial intelligence literacy and computational thinking in Chinese university students. Scientific Reports, 15(1), 42708.

Singh, R., Akram, S. V., Gehlot, A., Buddhi, D., Priyadarshi, N., & Twala, B. (2022). Energy system 4.0: Digitalization of the energy sector with inclination towards sustainability. Sensors, 22(17), 6619.

Chang, L. C., Lin, H. R., & Lin, J. W. (2024). Learning motivation, outcomes, and anxiety in programming courses—A computational thinking–centered method. Education and Information Technologies, 29(1), 545-569.

Ghanbaripour, A. N., Talebian, N., Miller, D., Tumpa, R. J., Zhang, W., Golmoradi, M., & Skitmore, M. (2024). A systematic review of the impact of emerging technologies on student learning, engagement, and employability in built environment education. Buildings, 14(9), 2769.