Expanding content and integrating digital and artificial intelligence technologies into a smart petrology course: A research-informed approach to teaching
Abstract
Advances in Emerging Engineering Education and the digital transformation of geoscience education are placing new demands on petrology courses, including the development of data literacy, systematic thinking, and adaptability to engineering practices. Conventional instruction still centers on memorization and classification, while constraints involving time, location, and safety restrict fieldwork and laboratory activities. Digital tools remain supplementary rather than being integrated into course content and instructional designs. Research findings are difficult to translate into teaching, leaving students familiar with rocks but unable to distinguish them reliably or apply their knowledge in practice. To address these problems, this study developed a research‑informed model that combined curriculum restructuring with digital and AI‑enabled instruction. Its three‑tier framework of "core streamlining, cross‑disciplinary supplementation, and frontier transformation" reorganizes petrological knowledge. It incorporated engineering case studies and converted advances in AI‑based rock and mineral identification and digital rock mechanics into teachable modules. An integrated "six‑in‑one" collection of smart teaching resources supported human‑machine collaboration in the classroom and a data‑driven feedback loop throughout the teaching process. The model was evaluated in terms of knowledge acquisition, integrative thinking, and replicability using evidence from comparable international educational reforms and first‑class courses at Xinjiang University that have been recognized at the provincial and ministerial levels. The findings indicate that the approach improves students' practical ability to identify rocks and minerals, promotes evidence‑based geological systems thinking, and strengthens the connection between theoretical knowledge and engineering practices. This approach, implemented at Xinjiang University, offers a transferable model for digital and AI‑enabled petrology instruction under comparable conditions, pending multi‑institutional validation.
Document Type: Original article
Cited as: Hu, C., Li, X., Feng, S., Quaye, J. A., Qi, D., Zhang, X. Expanding content and integrating digital and artificial intelligence technologies into a smart petrology course: A research‑informed approach to teaching. Higher Engineering Education Transformation, 2026, 1(3): 33‑44. https://doi.org/10.46690/heet.2026.03.01
DOI:
https://doi.org/10.46690/heet.2026.03.01Keywords:
Petrology , feeding research back into teaching , digital-intelligent transformation , smart course , geoscience education reform , AI-human-machine collaborationReferences
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