knowledge loss, and the adoption of AI-enabled knowledge systems. Together, these perspectives provide a holistic view of the “double talent gap”. The following sections outline the research design, data collection methods, and analytical intent for each participant group. 3.1 Educational Pathways Analysis To assess the factors influencing entry into the mining talent pipeline, a structured survey was administered to over 100 university students across North America. Participants ranged from first-year undergraduate students to PhD candidates. While none of the surveyed students were enrolled in mining-specific programs, a broad range of academic disciplines were represented. The student survey was divided into two primary sections. The first section examined prevailing perceptions of the mining industry, including social impact, environmental considerations, career attractiveness, and personal interest in pursuing mining-related careers. Questions used Likert-scale ratings, multiple-selection items, and open-ended responses to capture both quantitative trends and qualitative nuance. The second section of the survey evaluated the role of information exposure. Participants were presented with standardized information on the mining industry, including projected demand for minerals, workforce needs, compensation data, and the industry’s role in the energy transition. After reviewing this material, respondents were asked to reassess their likelihood of considering a career in mining. This design enables evaluation of whether limited awareness and educational exposure constitute significant barriers contributing to the declining entry of new professionals into the sector. 3.2 Industry and Workforce Analysis A parallel methodology was applied to the industry workforce analysis. A structured survey was administered to over 20 company and industry professionals worldwide, ranging from government representatives to company executives. The first section examined perceived barriers preventing student entry into mining. Considerations included graduate preparedness, existing training practices, and perceived gaps between academic and industry expectations. The second section examined the feasibility and current adoption of AI-enabled knowledge systems. Questions explored the use of digital tools for training support and barriers to broader implementation. Emphasis was placed on whether AI systems could support the transfer of knowledge from experienced professionals to new hires. 3.3 Data Integration, Scope, and Limitations Student and industry data were analyzed in parallel and then integrated within the Double Talent Gap framework. The quantitative results were used to identify distributional patterns across responses, while qualitative inputs provided interpretive depth and highlighted areas of divergence between perception and operational reality.. Taken together, insights from both groups informed the discussion presented later in the paper. 97
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