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<p>The mining industry is rapidly transforming through AI, but mining education lags behind, creating a skill gap between graduates and workforce needs. This study examines the integration of AI into mining curricula, drawing on insights from students, educators, and industry to align curricular changes with practical and professional requirements. A survey of 263 U.S. students, educators, and industry professionals showed strong consensus on several key areas. The urgency of integrating AI into the mining curriculum was rated as high as (4.74 ± 0.16) and the need for curriculum flexibility reached (4.68 ± 0.17). Both findings were statistically significant. In addition, statistically significant differences emerged in participants’ familiarity with and preparedness for applying AI in practice, particularly among industry. There was also strong agreement, with a mean as high as (4.85 ± 0.10), that AI knowledge will enhance students’ career prospects. Moreover, qualified faculty and active industry collaboration were the primary enablers of AI integration, whereas the primary obstacle was a lack of AI teaching expertise. These findings highlight the need for curricula that integrate computational and domain-specific skills through experiential learning, supported by faculty development and industry partnerships.</p>

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From Foundation to Future: Revisiting AI Integration in Mining Engineering Education Through Current Perspectives of Students, Educators, and Industry

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Rana Alhaj-Bedar,
  • Sarah Wilson,
  • Zach Agioutantis,
  • Steven Schafrik,
  • Ali Moradi,
  • Pedram Roghanchi

摘要

The mining industry is rapidly transforming through AI, but mining education lags behind, creating a skill gap between graduates and workforce needs. This study examines the integration of AI into mining curricula, drawing on insights from students, educators, and industry to align curricular changes with practical and professional requirements. A survey of 263 U.S. students, educators, and industry professionals showed strong consensus on several key areas. The urgency of integrating AI into the mining curriculum was rated as high as (4.74 ± 0.16) and the need for curriculum flexibility reached (4.68 ± 0.17). Both findings were statistically significant. In addition, statistically significant differences emerged in participants’ familiarity with and preparedness for applying AI in practice, particularly among industry. There was also strong agreement, with a mean as high as (4.85 ± 0.10), that AI knowledge will enhance students’ career prospects. Moreover, qualified faculty and active industry collaboration were the primary enablers of AI integration, whereas the primary obstacle was a lack of AI teaching expertise. These findings highlight the need for curricula that integrate computational and domain-specific skills through experiential learning, supported by faculty development and industry partnerships.