The increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human–AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators’ technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology.

Advancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision support—characterized by the AI's decision-making control—to observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial well‑being. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirm‑or‑override AI systems that sustain motivation and engagement, boosting retention and productivity.