Toward Emotion-Aware AI Robot Engagement: A Dual-Stream Learning Framework for Informal Science Communication in Diverse Cultural Contexts

To enhance learner engagement in traditional science communication, we innovative detection framework that based on long short-term memory (LSTM) networks. This framework equips AI robots with the capability to actively track fluctuations by combining sentiment-driven analysis and adaptive prediction mechanisms. First, we evaluate emotional attitudes on platforms such as YouTube and Bilibili, utilizing an emotion scoring system to capture shifts in learner preferences and interest levels, thereby creating a curated evaluation dataset for science content. Next, we enhance the robot’s facial emotion recognition performance during real-time interaction through convolutional neural network (CNN) training, which enables more accurate and personalized content recommendations. Finally, we integrate these features through an LSTM network, allowing the AI robot to dynamically assess and respond to learner interest. This comprehensive framework enhances emotional feedback mechanisms, enabling precise and real-time content recommendations. It also optimizes the role of AI robots as interactive science communicators to boost scientific literacy and engagement. This study contributes to science communication by enhancing the predictability of learner interest. It enables AI robots to interpret emotional responses to scientific texts with greater precision and personalization.