The implementation of learning recommender systems based on artificial intelligence (AI) for the training and development of employees presents a promising avenue for personalizing workplace learning, while simultaneously reducing the time and resources typically allocated to personnel developers. Nevertheless, the requisite quantity of personal data gives rise to concerns regarding privacy, which in turn affects the utilisation of such systems. This study examines the influence of perceived trustworthiness on the relationship between privacy concerns and the intention to use such systems. An online experiment was conducted to investigate the perception of a simulated AI-based learning recommender system. The results indicate that there is a negative influence between privacy concerns and perceived trustworthiness, while perceived trustworthiness exerts a positive influence on the intention to use. In particular, benevolence, as one facet of perceived trustworthiness, was found to mitigate the impact of privacy concerns. The study underscores the significance of a transparent and user-centred learning recommender system design that facilitates control over personal data and fosters trust. Further research should integrate additional variables, such as user control and privacy risk/benefit calculations, to gain a more comprehensive understanding of the relationship between privacy, privacy concerns, trust, and system use.

The complete article is a chapter of the book "Human Interaction and Emerging Technologies (IHIET-AI 2025): Artificial Intelligence and Future Applications".