Simplifying Gamification in Practice: A Design Model for Training Transfer-Oriented Industrial Learning

Abstract

Gamification has become one of the most sought-after strategies to enhance learners’ engagement and motivation, especially in digital learning environments. Within the organisational and industrial training context, the adoption of gamification has expanded, with Learning Management Systems (LMS) serving as a primary vehicle for workforce development. Industry 5.0 requires integration with cyber-physical systems, Artificial Intelligence (AI) and human-centred production principles that impose a continuous increase on workers for upskilling and reskilling. Nevertheless, a gap exists between the potential of gamification and what most implementations deliver. The main challenge is that, in the industrial LMS context, gamification is often driven more by platform accessibility than by instructional design. Game mechanics such as points, badges, and leaderboards are widely used for their availability rather than for their effect on the cognitive processes required to achieve learning objectives. The availability-driven approach can initially generate engagement, but it becomes more challenging to achieve Training transfer (TT) of competencies in industrial training, especially given the heterogeneity of industrial workers across experience, digital literacy, and motivational orientation, making a unified configuration unfeasible to sustain across the learners’ population. Compounding those challenges are the architectural constraints of existing LMS platforms, which might limit the consolidation of behavioural data across gamified components. This study introduces the SIGAT-IL Model (Scalable, Iterative, Gamification, Adaptive, Transfer, Industrial Learning) as a structured design framework to address those implementation challenges. Through a structured synthesis of current literature and established theoretical frameworks, the study identifies four implementation challenges: technological constraints, pedagogical-technical misalignment, learner heterogeneity, and scalability limitations, and proposes corresponding design principles. This model is centred around five established theoretical frameworks: Self-Determination Theory (SDT), Cognitive Load Theory (CLT), the Mechanics-Dynamics-Aesthetics (MDA) framework, the TT framework and the adaptive learning paradigm. The model does not prescribe a fixed configuration; it offers coherent, interconnected design principles that streamline implementation decisions and support contextually appropriate outcomes. The model repositions TT, defined as the generalisation and maintenance of trained skills in the operational workplace, as the primary criterion for evaluating gamification design choices, shifting focus from engagement metrics towards evidence of meaningful applied learning. The study concludes that repositioning TT as the primary evaluative criterion, rather than engagement metrics alone, is the essential shift required for more effective industrial gamification. These findings point to the need for longitudinal empirical validation of the model in operational industrial settings and comparative studies of structural, content and hybrid gamification configurations.