medIAte

The medIAte project arises in response to the growing deployment of AI-based decision-support systems in industrial settings. These systems are designed to assist or automate part of the operational decision-making process and are commonly seen as a mean to boost productivity and enhance the reliability of operations. However, their specific effects on human factors—particularly the cognitive, emotional, and behavioural dimensions of operators—remain largely unexplored. Although such systems can alleviate cognitive workload and facilitate decision making under certain conditions, there is a concern that excessive automation may diminish workers’ sense of autonomy, motivation, and overall engagement, ultimately impacting their well-being and performance. In contrast, maintaining a high degree of operator autonomy and decision-making power might foster positive psychosocial outcomes. In this context, the medIAte project is designed to examine how varying levels of AI decision support affect these psychological dimensions and worker performance in a production environment. To address these issues, two main hypotheses guide the research. One hypothesis posits that a high level of automation in the decision-making process could negatively affect operators by reducing their motivation, engagement, and performance. The alternative hypothesis suggests that granting operators a high degree of autonomy will have a positive impact on these same variables. To test these hypotheses, the project employs an iterative and rigorous experimental approach that unfolds in four phases. First, an online study involving 500 participants will evaluate their attitudes, perceptions, behaviours in response to different levels of AI integration (ranging from no AI support to partial or full automation). Next, a laboratory experiment with 100 novice operators will delve deeper using a multi-method approach that includes perceptual, physiological, and observational measurements. This is followed by a replication of the experiment with 30 experienced operators to enhance ecological validity by considering professional experience. Finally, a longitudinal study will be conducted over a six-month period in factory conditions, allowing us to observe the medium-term effects of AI integration through repeated measurements. The findings from these studies are expected to generate robust empirical evidence that will lead to several international peer-reviewed publications in high-impact journals. In practical terms, the project will provide concrete recommendations for industrial decision-makers and system designers on how to implement AI in a way that enhances both productivity and operator well-being. Additionally, medIAte will contribute to the enrichment of academic and professional training programs at its partner institutions by providing insights on the operational use of AI-based decision-support systems. The project is an interdisciplinary collaboration among researchers from the Institut d’Administration des Entreprises de Lyon (iaelyon), the École Nationale Supérieure des Arts et Métiers (ENSAM Cluny), Polytechnique Montréal, and the Université du Québec à Montréal (UQAM), integrating perspectives from management sciences, engineering, and work psychology.