Online learning platforms have become popular tools for delivering education, offering convenient and affordable learning opportunities to people worldwide. However, the integration of camera monitoring and AI learning analytics raises critical questions about their pedagogical effectiveness and user acceptance. This study investigates the efficacy of two distinct AI learning analytics in camera-monitored virtual classrooms: (1) Dashboard that analysis real-time biometric data (facial expressions, body movements, etc.) into performance reports, and (2) Learning moment, with which AI captures screenshots when detecting high student engagement (via the same biometric indicators) and shows to students. Through a 2 × 2 × 2 factorial experiment (N = 268), participants completed sequential 5-min burn knowledge courses with quizzes, receiving assigned feedback after the initial session. Results demonstrated that both tools enhanced learning performances, yet reduced platform reuse intent. Critically, concurrent use of both tools diminished dashboard’s efficacy, which may be due to students’ aversion to AI surveillance and privacy violations. While class size showed no significant moderating effect on learning performance, one-to-many classes mitigated the negative impact of analytics on reuse intent, suggesting peer presence mitigates surveillance anxiety. These findings reveal a tension in AI-augmented education: real-time analytics improve pedagogical outcomes but trigger privacy-efficacy trade-off. Our research provides critical design insights for camera-monitored learning platforms.