Generative artificial intelligence（AI）has emerged as a pivotal tool in the creation of science popularization literature; however，its capability in this domain remains subject to skepticism when compared to human authors. To evaluate the sciencepopularization writing competence of generative artificial intelligence，a four-dimensional evaluation framework was developed，comprising readability，engagement，scientific accuracy，and dissemination effectiveness. Using“microbiology”as the thematic focus，sciencepopularization works produced by human creators，DeepSeek，ChatGPT，and ERNIE Bot were systematically examined. The results indicate that both the initial-prompt and deep-prompt versions generated by ChatGPT showed no significant difference in overall scores relative to the human-authored version，with the deep-prompt version exhibiting a significantly superior performance in terms of readability. Moreover，the deep-prompt version of DeepSeek outperformed the human version in the dimensions of engagement and dissemination effectiveness. Notably，evaluators correctly distinguished AI-generated works in less than 55% of cases and showed a tendency to attribute high-scoring works to human creators. These experimental findings suggest that generative artificial intelligence holds the potential to substitute for humanscience popularization creators，thereby prompting the proposal of a novel“human-machine collaborative sciencepopularization creation”model. The study further calls upon the academic community to critically examine the phenomenon of the“Hallucination of Human’s Ability”.

Based on the results of the National Civic Scientific Literacy Sampling Survey，this study reveals that the scientific literacy of Chinese youth（aged 18~35）exhibits three distinct characteristics：high-level，regional imbalance，and cognitive contradictions. The findings indicate that the proportion of youth with qualified scientific literacy significantly surpasses the national average. Notable disparities exist across regions：developed areas such as the Yangtze River Delta outperform western and rural regions. Highly educated and knowledge-intensive vocational youth form a clear advantage group. Scientific interests are predominantly concentrated in specific domains，with a heavy reliance on digital media and interactive scenarios for information acquisition. The field of artificial intelligence presents the characteristics of“high activity coinciding with anxiety coexisting”，reflecting the deep contradictions in digital transformation. To advance high-level technological self-reliance and further foster an innovation-driven nation，this study proposes the following recommendations：Guided by policy frameworks，strengthen the foundation for enhancing youth scientific literacy；Construct a layered and classified system for accurately improving the scientific literacy of young people；Build a youth science popularization ecosystem with collaborative participation of multiple stakeholders；Actively embrace artificial intelligence，with Guiding young people to grasp the technological dividends and ethical boundaries.

The popularization of frontier scientific achievements plays a pivotal role in enhancing public understanding and engagementwith science. With the widespread application of generative artificial intelligence（GenAI），its potential performance of popularizing frontier scientific achievements has become a critical topic. Unlike the generation of general science popularization content，GenAI encounters distinct challenges when addressing topics related to frontier scientific achievements. Firstly，there is a paucity of sufficient online textual data for GenAI training.Secondly，these topics often entail relatively higher uncertainty，necessitating a more cautious approach to content formulation. This study conducts a comparative analysis of science popularization content on frontier scientific achievements generated by traditional science popularizers and that produced by GenAI models. The results indicate no significant disparities between the two in terms of scientific accuracy and source reliability. However，the content generated by GenAI models tends to exhibit a more positive emotional tone compared to that produced by traditional science popularizers. Based on these results，this study suggests that while the risk of GenAI disseminating misinformation may be limited，it has the potential to reinforce existing stereotypes. Consequently，GenAI can not entirely supplant traditional science popularizers within the current framework of science popularization.

Through a questionnaire survey of over 100 college students across the country，the current situation of science popularization reading among college students was preliminarily sorted out，and the main problems and corresponding countermeasures were analyzed. The results show that college students generally recognize the importance of science popularization reading，but their actual reading is insufficient，and they lack systematic reading plans. At present，the science popularization reading promotion activities for college students are faced with problems such as few brand projects，single activity forms，and a lack of innovation，which，to some extent，affect the effective implementation of science popularization reading for college students. Given these problems，it is suggested to strengthen ideological guidance，refine reading needs，and create a good campus reading atmosphere.

To strengthen the national capacity for science popularization and further implement the initiative to enhance the scientific literacy of all citizens，the China Association for Science and Technology（CAST），in collaboration with the National Bureau of Statistics，conducted the 14th national sample survey on Chinese civic scientific literacy in 2024. This survey provided critical data for the evaluation of the“Outline of the National Scheme for Scientific Literacy（2021—2035）”during the 14th Five-Year Plan period，as well as for achieving the goal of raising the proportion of citizens with scientific literacy to over 15% by 2025. The results show that the scientific literacy of Chinese citizens has been improving at an accelerating pace，with the proportion reaching 15.37% in 2024. The imbalance in scientific literacy development has been alleviated to some extent，and the population with basic scientific literacy is now substantial. The internet has further solidified its position as the primary channel for citizens to access scientific and technological information，while science and technology museums have demonstrated higher visitation and utilization efficiency among various science popularization infrastructures. Overall，Chinese citizens increasingly value science，adopt rational and pragmatic attitudes，and support innovation，with further improvements in critical thinking and scientific awareness. The development of artificial intelligence（AI）enjoys a strong public foundation，with citizens holding positive attitudes and ample confidence toward AI applications，though some concerns remain about job displacement.

This paper focuses on the synergistic developmental path between the scientificity and narrativity of science popularization. It systematically explains the mechanism for AIGC-driven synergistic optimization of scientificity and narrativity，analyzes the dual functions of AIGC in enhancing scientificity and optimizing communication，as well as the multidimensional issues behind its usage. Subsequently，it proposes corresponding optimization strategies for issues such as the detrimental effects of AI hallucinations on scientific rigor，the technical bottlenecks in generating highly complex scientific content，and the conflicts between instrumental rationality and value rationality. Namely，establishing multiple mechanisms to suppress AI hallucination generation，conduct narrative transformation for highly complex science popularization content，and build a synergy mechanism between instrumental rationality and value rationality.

University museums play a unique role in science popularization education. The development of science popularization courses in these museums not only extends formal education but also serves as a vital channel for disseminating scientific knowledge. In the new era，it is essential to explore how university museums can effectively utilize their rich and distinctive collections for educational purposes. This study focuses on the University of Southern California Pacific Asia Museum as a case study to examine its practice and experience in developing science popularization courses based on constructivist theory. The findings reveal that the museum’s courses feature clear goals，diverse content，a varied teaching staff，and a robust evaluation system. The study proposes a four-dimensional approach to course development in university museums：setting clear course objectives，infusing distinctive content，ensuring adequate faculty support，and enhancing development through evaluation. This framework aims to provide guidance for developing science popularization courses in Chinese university museums and to support the effective implementation of science education activities in these institutions.

The effective communication of scientific information in popularization short videos requires the collaboration of multiple modal discourses. The integration，complementarity，and reinforcement among these multimodal discourses facilitate audiences’understanding of complex scientific knowledge. Based on a multimodal discourse analysis framework，this study explores the narrative strategies of science popularization short videos intending to enhance their effectiveness. This study selects thescience popularization short videos from the Bilibili account“Huazha Hua Xiaolao”as the research object，analyzing the interaction among visual，linguistic，and auditory modal discourses within the videos. Findings indicate that multimodal narratives enhance the narrative expressiveness of science popularization short videos，stimulate emotional expression，and expand cross-media storytelling capabilities. On this basis，this paper proposes the following narrative strategies：scene-based narrative contextual optimization，deep integration of emotions and narrative cues，and cross-platform multimodal storytelling with full-link dissemination. These strategies aim to provide theoretical and practical guidance for the creation and dissemination of science popularization short videos.

In the context of convergence media，the field of meteorological science popularization is showing a trend towards interactivity and visualisation. Through case analysis，it has been found that interactive visualisation in meteorological science popularization communication exhibits characteristics such as intertwined narrative structures，multi-dimensional interaction among narrative subjects，flexible shifts in narrative perspectives，and multi-modal narrative discourse. To promote interactive and visualised dissemination of meteorological science popularisation，a multi-subject collaborative meteorological communication interactive model can be constructed in the future. This model should include dialogue mechanisms involving policymakers（including venues），science popularization creators，and diverse audiences，innovative cross-media communication models for meteorological scientific achievements，and develop a technical platform for concrete meteorological science popularisation scenarios.

The emergence and rapid development of generative artificial intelligence（GenAI）have greatly changed the current science popularization ecology. It not only effectively empowers current science popularzation creators，making the production of science popularization content more convenient and faster，but also directly intervenes in the current socialized collaborative science popularization pattern as a non-human actor. To better realize the benign intervention of GenAI in the current ecological pattern of science popularization，it is first necessary to systematically analyze its strategy bias as a science communicator when generating science popularization content and the possible differences between its science popularization strategy use and that of human science popularization creators. Based on the method of algorithm auditing，this paper finds，through coding and analyzing the science popularization content that representative GenAI tools generated at home and abroad，that GenAI has indeed adopted a series of diversified discourse strategies when producing science communicators，and there are certain differences between the discourse strategies adopted and those of human science communicators. Overall，it presents a characteristic of being inclined to be microcosmic and having an ambiguous identity. There are also considerable differences in the discourse strategies of large models based on different corpora within them，and they may need to be treated differently. This result may provide experience and reference for further improving the regulation and guidance of science popularization by GenAI in the future.