From foundation to future: Revisiting AI integration in mining engineering education through current perspectives of students, educators and industry

The mining industry is rapidly adopting artificial intelligence (AI), machine learning (ML) and autonomous systems to improve safety, productivity and efficiency. However, mining engineering education has not kept pace, leaving a widening gap between curricula and workforce needs. This challenge is intensified by declining enrollment as the 14 ABET-accredited U.S. mining engineering programs graduate only a few hundred mining engineers annually, far fewer than the number needed by the industry. To inform curriculum reform, this study analyzed perspectives from 263 students, educators, industry professionals and government representatives on integrating AI into mining education. Although stakeholders differed on the degree of curriculum flexibility, they agreed that graduates must be prepared for complex, data-intensive and uncertain workplaces. Findings revealed limited student exposure to computing, statistics and algorithmic thinking. Respondents favored curricula that combine technical training with critical discussion of labor impacts and responsible real-world application. They also preferred applied capstone projects as the primary method for teaching and assessment. Background Rapid advances in AI and automation have raised concerns about whether mining engineering curricula meet industry needs. Although previous studies emphasized workforce preparation and industry-academia collaboration, they did not define needed curriculum reforms or incorporate broad stakeholder perspectives. This study addresses that gap by examining how students, educators and industry professionals prioritize AI-related skills and knowledge. Because curriculum reform is shaped by diverse social, educational and economic interests, effective change requires a participatory, adaptive approach linking education with professional practice and workforce demands. Methods The study followed a pragmatic mixed-methods approach. The survey was developed and then validated through cognitive interviews and expert review. It included Likert-scale, multiple-choice, ranking and open-ended questions and was distributed at major mining conferences including the MINEXCHANGE 2025 SME Annual Conference & Expo, Women in Mining USA National Conference and the U.S. Rock Mechanics/Geomechanics Symposium. Quantitative data were analyzed using statistical methods such as ANOVA and chi-square tests, while qualitative responses were thematically analyzed to extract deeper insights. The complete study flow diagram is shown in Fig.1. Fig. 1 Flow diagram illustrating the sequential research stages of the study. The survey was administered in person between February 2025 and June 2025, yielding 263 responses (Fig. 2). Low participation among government representatives was attributable to significant workforce reductions in key agencies during the study period. Results and discussion Across all stakeholder groups, there was strong agreement on the need to incorporate AI applications into mining education, with industry respondents scoring the highest and showing minimal variability. This reflects the industry’s direct exposure to AI tools across various operations, such as autonomous haulage systems, predictive maintenance and data-driven exploration. Professors, while generally supportive, have shown greater variability in their responses, likely due to varying levels of familiarity with and confidence in teaching AI content. Students showed high motivation to learn AI, with (M = 4.50, ±0.14) mean agreement on its importance, but only moderate familiarity (M = 3.39, ±0.46) and confidence (M = 3.65, ±0.25), indicating a gap in exposure and foundational knowledge. Fig. 2 Distribution of study participants by stakeholder group. The main barrier to AI integration was the lack of expertise in teaching AI, which was ranked as the most significant obstacle by all groups, while collaboration with the mining industry and qualified educators were ranked as the most important resources. Students expressed a strong preference for experiential learning formats such as hands-on projects, simulations and real-world problem-solving. Open-ended responses emphasized that “integration of AI would require robust fundamental/engineering knowledge of the subject matter,” and that AI “will open up other opportunities for the students both in and outside of mining.” The results indicate that successful AI integration into mining engineering curricula requires navigating institutional constraints while maintaining a shared commitment to equipping graduates with strong disciplinary foundations and adaptable, practice-oriented skills. Industry professionals identified data analysis and interpretation as the most frequently used skill, followed by ML and generative AI models. Domain-specific knowledge and ethics received less emphasis, reflecting the industry’s preference for practical data and algorithmic competencies. These results are shown in Fig.3. Conclusions Findings suggested that enhancing mining engineering education for the AI era requires more than the mere inclusion of technical content. It necessitates a strategic and holistic integration of emerging skills, mindset shifts and institutional capacity. We suggest a model of strategic integration that aligns technological competencies with mindset cultivation, emotional reassurance and pedagogical capacity building. To guide implementation, the following recommendations are offered: Fig. 3 Industry-reported AI skills and knowledge usage. Embed mindset and resilience training: Incorporate modules on adaptability, ethical reasoning and problem-solving under uncertainty alongside technical AI content to enable emotional preparedness and opportunity-oriented thinking.Prioritize faculty development through industry-academic partnerships: Establish structured professional development programs codesigned with industry partners to ensure instructional content is current, relevant and practice-oriented.Develop formal mechanisms for lifelong AI learning: Offer microcredentials, short modular courses and industry-sponsored training accessible to both alumni and students to foster ongoing competency in AI tools and applications.Adopt experiential and project-based learning centered on AI: Integrate simulations, internships and collaborative projects into the curriculum to support the application of AI concepts in authentic mining contexts. Sequence AI education after core mining competencies: Ensure students build strong foundational/engineering knowledge before engaging with AI, as integration of AI would require robust fundamental/engineering knowledge of the subject matter. Strengthen academia-industry collaboration: Facilitate guest lectures, shared research initiatives and access to real-world data sets to align course content with industry needs and provide practical exposure.Address faculty expertise gaps: Invest in training and resources so educators can confidently teach AI-related content, as “lack of expertise in teaching AI” was identified as the most significant obstacle. Selected references Mitra R, Musingwini C, Neingo P, Adam Z (2018) Curriculum review process at the school of mining engineering at the university of Witwatersrand. Int J Georesources Environ 4(3):54–58Shay S (2015) Curriculum reform in higher education: a contested space. Teach High Educ 20(4):431–441

From foundation to future: Revisiting AI integration in mining engineering education through current perspectives of students, educators and industry

Authored by: Rana Alhaj-Bedar, Sarah Wilson, Zach Agioutantis, Steven Schafrik, Ali Moradi and Pedram Roghanchi

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Mining Engineering’s Weekly Rundown: Episode 73

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To view this article you must be a member of SME/UCA.

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Mining Engineering’s Weekly Rundown: Episode 73

Get your SME Foundation VIP Access Conference Pass to MINEXCHANGE 2027

“Check the box” and help support the SME Foundation

Mining Engineering’s Weekly Rundown: Episode 73

Get your SME Foundation VIP Access Conference Pass to MINEXCHANGE 2027

“Check the box” and help support the SME Foundation

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