1. CONTEXT AND PROBLEM STATEMENT Delivering the minerals the world urgently needs—faster, smarter, and more responsibly—requires not only technological innovation but systematic workforce preparation for the humans who will operate new systems. Yet recent scoping reviews reveal a critical disconnect: despite decades of automation deployment in mining, human factor principles in technology design and evaluation have not become common practice, unlike aviation or healthcare where such integration is standard (Codoceo-Contreras et al., 2024; Lund et al., 2024). Mining 4.0 literature emphasizes technological capabilities while leaving “not much knowledge on how to develop the skills of the workforce that mining companies already have” (Lund et al., 2024). At the operational level, skills development remains “haphazard and unstructured” (Chirgwin, 2021), with control room operators acquiring knowledge “piece by piece in hands-on training from more experienced operators”—a process identified as “a key bottleneck for enhancing human capacity” in mineral processing (Li et al., 2011). The studied division of a major Chilean state-owned copper company exemplifies these challenges at an acute scale. The definitive closure of the division’s smelter—driven by environmental sustainability imperatives and decarbonization pressures—forced workforce reconversion, with 14 workers relocated to the Fire Refining Area. This organizational disruption coincides with technological modernization through two capital projects: an Advanced Rotary Furnace (ARF), a 300-ton unit replacing traditional reverberatory technology with integrated DCS control systems, enhanced energy efficiency, and nitrogen agitation for improved chemical homogenization; and an Emissions Treatment Plant (ETP), addressing environmental compliance through combustion gas processing and heat recovery. Together, these projects represent a fundamental shift from experience-based manual operations to digitally-mediated process control. The fire refining workforce—60 workers averaging 44.8 years of age (range: 23–62) with 13.1 years of mean tenure—embodies the tension between accumulated expertise and technological transition. This mature, experienced workforce must now adopt new technologies, digital control systems, and collaborative methodologies, while the organization must prepare them rather than replace them. Preliminary diagnostic findings using a validated hierarchical productivity model revealed that these workers operate at only 40.6% of their theoretical productive potential—a deficit rooted in attitudinal rather than aptitudinal factors. The workforce demonstrates technical competence but motivational disengagement, with reconverted workers maintaining capabilities but having lost organizational purpose. Without proactive intervention, the organization risks crystallizing into what diagnostic narratives revealed as “resigned mediocrity rather than operational excellence.” Organizational change scholarship establishes that workforce adaptation depends not merely on training provision but on psychological and contextual factors: shared commitment and collective efficacy beliefs (Weiner, 2009), the interaction between implementation climate and innovation-values fit (Klein & Sorra, 1996), and individual dispositional, contextual, and process-related antecedents that shape reactions to change (Oreg et al., 2011). Simultaneously, adaptive learning research demonstrates that instructional effectiveness requires systematic understanding of learner characteristics, content architecture, and the interpretive layer through which workers perceive their capacity to learn (Martin et al., 2020). Yet a persistent integration 217
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