As AI-driven recommender systems become more persuasive, users are increasingly confronted with a trade-off between personalization and privacy. This study investigates how different explanation types in recommender systems influence users’ privacy concerns and trust, using both self-reported data and eye-tracking metrics. Drawing on the privacy calculus framework and trust theory, we employ a 2x5 factorial experiment involving 120 university students who interact with a course recommender system. The study introduces eye-tracking as a novel lens to assess cognitive responses to privacy-sensitive content within explanations. Our findings are expected to reveal how visual attention to personalized details correlates with privacy concerns and trust, thereby informing the design of transparent yet privacy-aware recommender systems. This research contributes to theory by extending the privacy calculus with cognitive measures and provides practical guidelines for explanation design in AI systems.