Teaching and Learning with AI Conference

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming modern mining operations, including exploration, mine planning, automation, safety, and production optimization, creating a growing need for mining engineering curricula to equip students with relevant AI- and ML-related skills. This study investigates the effectiveness of a newly developed AI in the Mining Industry course in equipping mining engineering students with suitable AI and ML competencies and examines how students’ perspectives toward AI and ML applications in the mining industry change after completing the course. The course was developed in response to a prior comprehensive need-assessment study and was accordingly designed and implemented. To evaluate the course impact, the study employed the first two levels of the Kirkpatrick evaluation model, focusing on student learning outcomes and changes in student perspectives, using pre- and post-course surveys to assess self-reported competencies and perceived relevance of AI in mining. Preliminary findings indicate improvements in students’ understanding of AI and ML concepts within a mining context, along with a shift toward more informed and positive perceptions of AI’s role in the mining industry. This study addresses a gap in the literature on AI integration in Mining engineering education and provides evidence-based insights to support curriculum development aligned with emerging industry needs. Keywords: Artificial Intelligence, Machine Learning, Curriculum Evaluation, Mining Engineering Education