Author(s)

Su Yang ¹, Chen Jie ¹, Wang Jiao ¹, Liu Yuzuo ¹

Affiliation(s)

¹ College of Earth Science and Engineering, Shandong Institute of Petroleum and Chemical Technology, Dongying, China

Corresponding Author

Su Yang

ABSTRACT

In response to the urgent demand for composite geological talents capable of "intelligent interpretation, data-driven analysis, and engineering decision-making" driven by the digital transformation of the energy industry, the traditional "Petroleum and Natural Gas Geology" course faces challenges such as lagging content, singular evaluation methods, and a lack of engineering context. Based on the Outcome-Based Education (OBE) philosophy, this study proposes and implements a teaching reform model characterized as "OBE-Led and AI-Empowered." First, applying the principle of Backward Design, a course objective system covering knowledge, skills, engineering capabilities, and comprehensive qualities was constructed, clarifying the core requirement of "solving complex petroleum geological problems." Second, the course content was reconstructed into a "Geological Intelligent Cognitive Pyramid" (Fundamentals Layer - Methods Layer - Application Layer), deeply integrating AI technologies with hydrocarbon accumulation theories. Third, an intelligent teaching mode driven by "Complex Basin Problems" was designed, utilizing an AI toolchain (e.g., intelligent logging interpretation, fault identification, migration simulation) to support Project-Based Learning (PBL) and case studies, thereby realizing full-process data-driven evaluation. Practice indicates that this model effectively bridges the gap between education and industry, significantly enhancing students' abilities in multi-source data fusion analysis and engineering decision-making. It provides a reference pathway for constructing a new teaching paradigm for the Resource Exploration Engineering major in the intelligent era.

KEYWORDS

The Outcome-Based Education (OBE), Artificial Intelligence (AI), Petroleum and Natural Gas Geology, Teaching Reform

CITE THIS PAPER

Su Yang, Chen Jie, Wang Jiao, Liu Yuzuo, AI-Empowered Teaching Reform Based on OBE Philosophy: An Exploration of the "Petroleum and Natural Gas Geology" Course for the Resource Exploration Engineering Major. Curriculum and Teaching Methodology (2026) Vol. 9: 7-15. DOI: http://dx.doi.org/10.23977/curtm.2026.090102.

REFERENCES

[1] Spady W.G. (1994) Outcome-Based Education: Critical Issues and Answers. American Association of School Administrators.
[2] Crawley E.F., Malmqvist J., Östlund S., Brodeur D.R. and Edström, K. (2014) Rethinking Engineering Education: The CDIO Approach (2nd ed.). Springer.
[3] Li Z. (2014) Analysis of the Outcome-Based Concept in Engineering Education Professional Accreditation. China Higher Education, (17), 7-10.
[4] Luo D. and Wang B. (2025). Exploration on the New Paradigm of AI-Empowered Engineering Education in Higher Education. Journal of Higher Education, 29, 16-19.
[5] Bond C.E. and Cawood A.J. (2021). A role for virtual outcrop models in blended learning – improved 3D thinking and positive perceptions of learning. Geoscience Communication, 4(2), 233-244.
[6] Patra S., Singha T.S., Kanvinde M., Mazumdar A., and Kanjilal S. Harnessing AI for Geosciences Education: A Deep Dive into ChatGPT's Impact, Geosci. Commun. Discuss. [preprint], https://doi.org/10.5194/gc-2023-7, 2024.
[7] Dramsch J.S. (2020). Chapter one - 70 years of machine learning in geoscience in review. In Advances in Geophysics, Moseley B., Krischer L., Eds. Elsevier, 61, 1-55.
[8] Elete T.Y., Nwulu E.O., Erhueh O.V., Akano O.A. and Aderamo A.T. (2024). Digital transformation in the oil and gas industry: A comprehensive review of operational efficiencies and case studies. International Journal of Applied Research in Social Sciences, 6(11), 2611-2643.
[9] Chiu T.K.F. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187–6203.
[10] Siemens, G., & Baker, R. S. (2012). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254.

Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
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