Concerns were frequently expressed regarding the reliability of AI outputs, data quality, confidentiality, and the limitations of AI in complex geological contexts. Many emphasized that activities such as geological classification, resource modelling, and exploration targeting rely heavily on contextual judgment and field-based experience—capabilities that AI cannot consistently replicate. Nevertheless, AI was acknowledged as a significant technological development for the industry. Rather than rejecting its use, most indicated that the sector must clearly identify appropriate and well-defined applications where AI can enhance efficiency while maintaining professional oversight. 6. Discussion The findings suggest that workforce capacity in the mining sector may be shaped by the interaction of pipeline contraction and institutional knowledge concentration, consistent with the structural logic of the Double Talent Gap framework proposed in this study. At the upstream level, a persistent disconnect appears between recognition of mining’s societal relevance and its perceived career attractiveness. Although students widely acknowledge mining’s importance to the energy transition and global infrastructure, entry into mining-specific disciplines remains limited. In the absence of structured educational exposure, early perceptions—often shaped by environmental concerns, safety assumptions, and lifestyle expectations—may form prior to informed engagement. These perception-based filters can divert potential entrants before they develop a concrete understanding of occupational pathways or technological roles. The alignment between student and industry responses is consistent with the presence of a Pipeline Gap associated with informational distance and limited visibility. Perceptions of technological identity further appear to mediate this process. While a substantial proportion of students recognize mining’s technological advancement, many remain uncertain, and a majority express preference for innovation-oriented sectors. Despite increasing integration of automation, data analytics, and digital modelling systems, mining does not appear to be consistently associated with a forward-facing technological narrative. The observed shift in perception following informational exposure suggests that some elements of career filtering may be responsive to improved awareness, although the persistence of such shifts over time would require further investigation. At the downstream level, industry responses indicate continued reliance on mentorship-based transfer of tacit knowledge. Moderate graduate preparedness ratings and extended time-to-productivity suggest that experiential development remains central to professional formation. Under conditions of demographic aging, declining inflow may increase training demands on a shrinking senior cohort, potentially slowing knowledge transfer and increasing operational sensitivity. Although AI-supported systems are viewed positively for documentation and information management, their application remains largely supportive rather than substitutive, and core expertise continues to reside primarily within experienced professionals. 105
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