Learning recommender systems (LRS) based on artificial intelligence (AI) (AI-LRS) are becoming increasingly integrated into workplace learning. Previous research on AI-LRS design focused on technical improvements and excluded users’ privacy concerns and perceptions with AI-based systems. This study combines a human-centered approach with research on privacy, AI-LRS evaluation and use, examining the effects of data disclosure (voluntary vs. mandatory) on AI-LRS user perception. 256 employees took part in an online experiment simulating an AI-LRS for workplace learning. Results indicate that employees perceive the system as more trustworthy and worry less about privacy when they receive more control. This positively influence both the intention to use the system and its overall evaluation. Our research introduces a novel, human-centered, AI-LRS design. It contributes to the understanding of privacy and trust mechanisms in human-AI interaction and offers implications for the design of human-centered, privacy-sensitive learning technologies.

This article is published in the "International Journal of Human-Computer Interaction" (2026).