Shanshan Guo

4 weeks ago | nature.com | Shanshan Guo |Huzaina Binti Abdul Halim |Mohd Rashid bin Mohd Saad

The AI era has ushered in a new wave of opportunities for enhancing classroom education, particularly in the realm of higher education. This study investigates the integration of AI and mobile learning technologies to promote innovation and reform in education. It explores how AI-driven platforms, using soft computing networks, can improve students’ critical thinking skills and foster deeper engagement with academic subjects. The research also examines the deployment techniques and procedures for incorporating mobile learning technologies into higher education settings. A comparison experiment is conducted to assess the effectiveness of the AI-driven system against traditional learning methods, revealing that AI-based learning enhances student motivation and practical skills. The study highlights the broader implications of AI in education, including its potential to facilitate global collaboration, enhance educational equity, and address the evolving needs of digitally connected learners. Finally, the paper suggests directions for future research, including the application of AI across various academic disciplines and the exploration of ethical considerations in AI-driven education.

1 month ago | nature.com | Shanshan Guo |Yun Chen |Yuanyuan Dang |Victoria Aranda

In public health emergencies, there is a critical need for accurate informational and emotional support to counteract misinformation and trauma. Online Health Communities (OHCs) serve as essential resources for real-time health counseling and support. This study investigates how OHCs facilitate the acquisition of informational and emotional support, crucial for guiding informed protective decisions. By integrating the Protective Action Decision Model (PADM) with social support theory, the research examines the impact of disaster-related information on patients’ decision-making within OHCs, aiming to optimize these platforms for public health response and preparedness. The study utilizes a dataset comprising 602 doctor-patient consultation dialogues from a Chinese OHC. Through text and sentiment analysis, the study quantifies the volume of information and sentiment, which serve as indicators of the level of informational and emotional support sought by patients. Environmental and social cues related to emergency situations are measured using disaster early forecast information and the volume of social media discussions on the emergency. Multiple linear regression models are employed to analyze the impact of these cues on patients’ behaviors, specifically their informational-seeking and emotional-seeking actions. It indicates that social cues have an impact on patients’ seeking informational support, while only in the high-uncertainty public health emergency, environmental cues are positively correlated with patients’ seeking both emotional and informational support. Additionally, stakeholder actions in the context of OHCs positively moderate the influence of environmental and social cues on individual protective actions to some extent. This study advances the understanding of OHCs by applying and empirically testing the PADM in a digital health context. It also explores the varying impacts of different types of public health emergencies on patient behavior within OHCs. The findings can guide healthcare providers and OHC administrators in enhancing support mechanisms, particularly during public health emergencies.