Under the background of a strong country in education and science and technology, the importance of science education is increasingly prominent. As an important goal of science education, the nature of science helps students better understand scientific knowledge, develop scientific thinking, and enhance curiosity and imagination. Teaching the nature of science in early childhood is crucial to ensuring that students have an accurate understanding of the nature of science. However, the survey results show that the overall development level of kindergarten graduates' understanding of the nature of science is low, and there are regional and school differences in China. Schools in the western region and schools with weak conditions are in a relatively vulnerable position. Besides, the survey results of kindergarten teachers also reveal that kindergarten teachers themselves have a poor understanding of the nature of science, and a serious lack of understanding of the importance of the nature of science. Based on this, this study proposes a multi-level framework for promoting the understanding of the nature of science in early education. At the macro level, it advocates for the early implementation of relevant educational practices and the optimization of resource allocation. the meso level, the focus shifts to institutional empowerment and ecological development, aiming to enhance science teachers' comprehension and appreciation of the nature of science, foster an open, diverse, and inclusive environment for exploration, and establish a coherent science education system bridging kindergarten and primary school. At the micro level, the emphasis is placed on the internalization of teaching practices and interactive pedagogies. Through modalities such as hands-on experiments, scientific dialogue, and home-school collaboration, the framework seeks to advance young children's grasp of the fundamental understanding of the nature of science.

As artificial intelligence increasingly permeates the field of education, early childhood science education is encountering both new opportunities and emerging challenges. Grounded in multimodal learning theory, this paper reviews the applications of artificial intelligence in scientific context creation, inquiry process support, and learning process evaluation, and analyzes the potential risks it may pose to child development, teacher professionalism, and educational ethics. The analysis indicates that artificial intelligence can enrich children’s learning methods and enhance the quality of science education through multimodal presentation, instant feedback, and continuous evaluation. However, its deep involvement may also weaken children’s experiential inquiry, diminish teachers’ professional judgment, and, in certain contexts, lead to the encroachment of technical logic upon educational values. In response, this paper proposes strategies across three dimensions: educational philosophies, teacher professionalism, and institutional safeguards. These strategies include upholding inquiry-based learning, strengthening teachers’ professional judgment, and improving ethical norms and data security mechanisms, aiming to promote the appropriate and effective empowerment of early childhood science education by artificial intelligence, ensuring that technological innovation genuinely serves children’s scientific learning and development.

In the new era, the promulgation of a series of laws, regulations and policy documents has put forward higher and more challenging requirements for early childhood science education. This study explores the requirements and challenges posed by the new era for early childhood science education in terms of educational objectives, teachers’ scientific literacy, technological empowerment, multi-agent collaboration and other aspects. By crafting its value system based on the five perspectives of basic view, systematic view, symbiotic view, construction view and digital-intelligent view, this study builds an independent exploration-oriented early childhood science education model, improves the teachers’ scientific literacy, and establishes a “six-party” collaborative ecological system for early childhood science education.

In the strategic context of the “Artificial Intelligence+” Action, enhancing national AI competence is a prerequisite for consolidating the foundations of an inclusive, intelligent society, activating New Quality Productive Forces, and securing a competitive national advantage. National AI competence can be tiered into two levels. The first is General AI Competence, designed for all citizens, and includes functional application, critical thinking, and ethical safety. The second is Domain AI Competence, aimed at professionals, focusing on process re-engineering, human-machine collaboration, and ethical governance. To advance this competence, we must move beyond spontaneous, fragmented individual efforts and establish it as a systemic public popularization endeavor. This requires strengthening top-level design, adopting a dual-track strategy of universal inclusion and targeted precision, consolidating the responsibilities of platforms and media, establishing social incentive and certification mechanisms, and building a continuously evolving popularization research support system.

It is evident that parks and science museums play a significant role in the development of the country's science popularization capability and the realisation of its high quality development. However, there are several issues that must be addressed in the development of science parks, such as the need for infrastructure upgrades, the lack of integrative, diverse and specialised content, and innovation in the application of scenes. The study is founded upon the question refer to how to promote the transformation of the function of science museums and its service. The study draws upon symbiosis theory, employing the analytical method of examining the historical evolution of parks and museums. In addition, an explanation is provided of the unique scientific and cultural value of parks, and of their role in the construction of a scientific and technological powerhouse. Moreover, a number of typical cases are selected for comprehensive analysis. The study proposes a series of strategies to facilitate the healthy development of science parks. These include the construction of new infrastructure for science popularization, the exploration of park resources, the establishment of a platform for research, and the enhancement of the technology that enables parks to develop. The overarching objective of these strategies is to effect a transformation in the functional orientation of science popularization position and transformation and upgrading of service mode.

With the widespread application of generative artificial intelligence in creative domains，AI is increasingly recognized as an autonomous co-creator，making human-AI co-creation a frontier topic in creativity research. Unlike conventional AI-assisted models，genuine human-AI co-creation requires humans to shift from task executors to “directors” and “gatekeepers”，maintaining proactive agency and leveraging their irreplaceable strengths in divergent thinking，contextual judgment，and authenticity control. However，current practices often fail to fully adhere to these critical boundaries，creative human-machine collaboration unleashs individual creative potential and promoting equality in creativity，and led to challenges accompanying the benefits concurrently，such as homogenization effects，weakened interpersonal collaboration，and copyright disputes. To promote sustainable development in human-AI co-creation，it is imperative to reshape public perceptions of creativity，advance evidence-based human-AI collaborative practices，systematically cultivate co-creation literacy，construct an ecosystem for human-human-AI co-creation，and establish ethical and institutional frameworks that balance democratization with incentives.

Deep synthesis technologies，exemplified by DeepSeek，are injecting powerful momentum into modern science communication，guiding the public’s understanding of science into a new normal. They are propelling public cognitive paradigms from “tool-assisted” to a novel stage of “cognitive enhancement”. However，this technological leap is not entirely smooth. While presenting numerous opportunities，it also harbors cognitive risks. This inherent tension reveals that while the public benefits from enhanced cognitive efficiency，expanded cognitive horizons，and transformed knowledge transmission paradigms through algorithmic tools like DeepSeek，they simultaneously face risks of cognitive displacement which includes systemic degradation of critical thinking，pathological fixation of cognitive dependencies，and hierarchical rigidification of cognitive disparities due to “cognitive outsourcing”. Faced with the technological empowerment value and potential cognitive risks demonstrated by tools like DeepSeek in AI science popularization，we must pursue a rational dialectical thinking dimension to advance development，constructe a three-dimensional collaborative framework integrating technology，cognition，and ethics. By restructuring critical thinking training mechanisms，refining technical usage standards，and strengthening social equity safeguards，we can foster a new ecosystem for AI science communication characterized by human-machine symbiosis and inclusive sharing，ensure technological capabilities genuinely serve the comprehensive development of the public as DeepSeek and similar deep synthesis service algorithms integrate with modern science communication.

The burgeoning of generative artificial intelligence is reshaping the paradigm of scientific knowledge dissemination，while its derivative phenomenon of “knowledge hallucination” profoundly deconstructs the foundational credibility of scientific communication. Drawing upon Latour’s actornetwork theory analytical framework，this paper posits AI knowledge hallucination as a product of systemic distortion or “betrayal” within the translation process. This occurs within a heterogeneous actornetwork due to the divergence of objectives and conflicts of interest among key actors. This phenomenon stems from four structural dysfunctions：Source networks suffer contamination from diachronic biases in data inscription and algorithmic goal alienation；Error-correction networks face institutional feedback deficits and temporal asynchrony conflicts hindering rectification; Translation chains endure professional discourse dimensionality loss and technical violence from cross-contamination within heterogeneous knowledge networks；Accountability networks descend into governance vacuums due to technological black-box obscurity and fragmented responsibility. To address these issues，this paper proposes a four-dimensional path for collaborative governance：“Source Purification” fortifies data foundations through a multi-centre knowledge certification system and blockchain traceability technology；“Process Streamlining” builds a closed-loop error correction network leveraging intelligent monitoring systems and dynamic knowledge repositorie；“Translation Optimisation” ensures knowledge fidelity via context-aware algorithms and human-machine dual verification mechanisms；“Anchoring Accountability” clarifies responsibility boundaries among diverse stakeholders through legal empowerment and transparent algorithmic disclosure. This research deepens understanding of the socio-technical nature of AI knowledge hallucination，advancing the development of a robust，trustworthy “AI+Science Communication” ecosystem centred on human-machine collaboration.

The application of generative artificial intelligence in the field of science writing is burgeoning. While empowering science writing，it also introduces unique challenges，such as scientific inaccuracies of science popularization works，the identity and ability recognition of science writers，and the “limited autonomy” of the audience under the implicit dominance of technology. It is suggested to guide the responsible and sustainable development of generative AI-assisted science writing from several aspects：Adhering to the bottom line of the scientificity of science popularization works；Strengthening the subjectivity of “human” of science writers and emphasizing the organic integration of instrumental rationality and value rationality；Enhancing the scientific and digital literacy of the public and creating a new environment for science writing that suits the current situation.