Okada, H.; Seno, S.; Chung, U.-i.; Ohkura, N.

Recent large-scale bibliometric analyses suggest that individual AI use can increase productivity while reducing downstream engagement and topic diversity. Here we ask whether collective AI deployment is associated with shared engagement. Using an Audience Response Engagement (ARE) system at NGS Expo 2025 (N=110 biomedical researchers), we captured real-time consensus and generated updated visualizations within minutes. Our data reveal a substantial gap between adoption and transparency: 93.6% of researchers use AI at least weekly, yet only 5.5% consistently disclose this usage; a 17-fold disparity. This pattern is consistent with systemic policy uncertainty (39.1% report unclear guidelines). Behavioral clustering identifies a High-Concern group (31%) as a candidate for targeted interventions: highest productivity yet lowest disclosure. These findings suggest that collective AI deployment in physical settings is associated with shared engagement.