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.

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.

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.

As the pace of global digital transformation accelerates，digital literacy has emerged as a cornerstone for enhancing citizens’ overall competence and bolstering national competitiveness. By analyzing and comparing strategies and policies for enhancing digital literacy in five representative countries/regions—the United States，the European Union，the United Kingdom，Singapore，and Japan-this paper proposes targeted recommendations for advancing China’s nationwide digital literacy.The study finds six elements consistently underpin these national strategies that regular evaluation of strategic progress，robust digital infrastructure，comprehensive digital-literacy frameworks，sustained cultivation of digital talent，wide-ranging digital-skills training programs，and multi-stakeholder cooperation mechanisms. Looking ahead，China should refine its development goals and supporting measures，continue to expand and upgrade its digital infrastructure，establish a national digital-literacy assessment framework and corresponding curriculum system，enhance digital services for vulnerable groups，strengthen the digital-talent pipeline，and foster closer collaboration among government，industry，academia，and civil society.

As the core ability of citizens to effectively participate in digital life，digital literacy has become a key indicator of national competitiveness. This paper systematically reviewed representative international and domestic definitions of digital literacy and distilled their underlying theoretical orientations，thereby illuminating the conceptual evolution of digital literacy. It aims to develop a theoretical system and practical mechanism of digital literacy with local characteristics. This study shows that the connotation of digital literacy has shifted from instrumental competence to a comprehensive competence system covering critical thinking and security. Current research shows three major trends，including conceptual expansion，exacerbation of the literacy divide，and the rise of algorithmic and AI literacy. In the future，it is imperative to develop a local digital literacy theoretical system and an education ecosystem involving multi-stakeholder collaboration，thereby responding to the new requirements in digital society.

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 arrival of the platform society poses new challenges to traditional citizens’ digital literacy，reflected in both visually-centred forms of content expression and algorithmically-driven mechanisms of content distribution. Based on an analysis of visual studies and platform studies，this paper argues that digital literacy in the platform society comprises two key dimensions：visual literacy and algorithmic literacy. Enhancing citizens’ digital literacy requires developing both visual interpretation，critique，and creative abilities, as well as algorithmic cognition，critique，and interactive abilities. Furthermore，it emphasizes that the collaborative enhancement of digital literacy in the platform society should be grounded in structural empowerment，guided by inclusiveness，and oriented toward future technological transformations.

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.