AI and Emerging Workforce Competencies

Permanent URI for this collectionhttps://hdl.handle.net/10125/112530

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    Guided by the Bot, Driven by the Worker: Task Crafting and Emerging Competencies in GenAI-Augmented Manufacturing
    (2026-01-06) Perozzo, Haiat; Ravarini, Aurelio
    As AI reshapes manufacturing, understanding how workers adapt and develop new competencies is critical. This study offers a grounded, step-by-step model of task crafting in a digitally evolving manufacturing company, highlighting how employees respond to misalignments by redesigning tasks, tools and routines. Based on interviews and observations, the findings reveal a recursive process driven by experiential knowledge, collaboration, and local innovation. Generative AI chatbots (e.g., ChatGPT) augment this process by supporting ideation, automation and informal learning, without displacing human agency. Generative AI supports micro-reskilling and hybrid competencies as a digital companion. This study contributes to job crafting literature by framing competence development as emergent and situated, and to Generative AI-in-manufacturing debates by showing how human-AI collaboration unfolds in practice. It offers implications for workforce transformation strategies aligned with Industry 5.0, where AI and human expertise co-evolve in dynamic, socially embedded ways.
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    Introduction to the Minitrack on AI and Emerging Workforce Competencies
    (2026-01-06) Snis, Ulrika; Norström, Livia; Vallo Hult, Helena; Saadatmand, Fatemeh
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    Guess, Learn, Repeat: Intelligent Learning System with Synthetic and Counterfactual Training in a GeoGuessr-Inspired Classification Task
    (2026-01-06) Goutier, Marc; Spitzer, Philipp; Zipperling, Domenique
    Training novices by experts is often costly and time-consuming. Alternatively, learning systems offer a scalable and automated alternative. However, learning systems offer another, yet underexplored advantage, over training with experts: Analyzing novices and providing personalized training. This study explores the use of synthetically generated images to improve novice image classification skills in a GeoGuessr-inspired classification task. By leveraging a counterfactual-based approach and synthetically generated personalized training data, we aim to enhance individual learning. In a controlled experiment where participants classify Google Street View images from four different cities, we compare the impact of personalized synthetic images against randomly assigned ones. Our findings indicate that personalized training improves classification accuracy, underscoring the potential of intelligent learning. These results highlight a promising direction for integrating synthetic data into adaptive training environments in game-like settings, paving the way for effective and personalized intelligent learning systems.