Adaptive AI-Driven Personalization of Science Communication Based on Real-Time Audience Feedback

Building on Li et al.'s (2025) findings about diverse audience preferences for communication techniques, this research introduces an AI-powered framework that continuously tailors science communication content during live interactions such as social media streams or webinars. The system treats narrative structure and language formality as controllable parameters modulated by real-time signals like engagement metrics and sentiment analysis. For example, it can inject relatable examples or switch to step-by-step walkthroughs when engagement dips, or increase formality and add data-driven references if credibility concerns arise. This approach moves beyond static preferences to moment-to-moment adaptation, integrates qualitative and quantitative engagement data, and employs NLP, computer vision, and reinforcement learning to optimize narrative adjustments. It addresses scientists' varying comfort levels by automating style adjustments, potentially reducing cognitive load for early career scientists and revolutionizing science communication with responsive, personalized content.

References:

1. Audience Impressions of Narrative Structures and Personal Language Style in Science Communication on Social Media. Grace Li, Yuanyang Teng, Juna Kawai Yue, Unaisah Ahmed, Anatta S. Tantiwongse, Jessica Y. Liang, Dorothy Zhang, Kynnedy Simone Smith, Tao Long, Mina Lee, Lydia B. Chilton (2025). arXiv.org.