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Artificial intelligence (AI) has emerged as a prominent topic in K-12 education recently. However, pedagogical design has remained a major challenge, especially among young learners. Guided by the Zone of Proximal Development theory and AI education research literature, this design-based study proposes an analogy-based pedagogical approach to support AI teaching and learning in upper primary education. This pedagogical approach is centered on human-AI comparison, where humans are gradually shifted from an analogue to a contrast to make visible the attributes, mechanisms, and processes of AI. To evaluate its effectiveness, a quasi-experimental study with mixed methods was conducted. The quantitative comparison shows that the participants in the experimental group learning with the analogy-based pedagogical approach significantly outperformed their peers with the conventional direct instructional approach in all three dimensions of AI knowledge, skills, and ethical awareness. Qualitative analyses further reveal its pedagogical benefits, including demystifying AI through relatable and engaging learning, supporting student comprehension and skill mastery, and nurturing critical thinking and attitudes. The analogy-based approach contributes to the field of K-12 AI education with an age-appropriate, child-friendly pedagogical approach. Notably, AI education should prioritize teaching for student understanding, and AI should be recognized as an independent subject with interdisciplinary applications.

Background: The recent discussion of introducing artificial intelligence (AI) knowledge to K-12 students, like many engineering and technology education topics, has attracted a wide range of stakeholders and resources for school curriculum development. While teachers often have to directly interact with external stakeholders out of the public schooling system, few studies have scrutinized their negotiation process, especially teachers' responses to external influences, in such complex environments. Purpose: Guided by an integrated theoretical framework of social constructionism, this research examined the process of how a teacher-initiated AI curriculum was constructed with external influences. The research focused on teachers' perspectives and responses in mediating external influences into local schools and classrooms. Methods: A 3-year ethnographic study was conducted in relation to an AI curriculum project among 23 Computer Science (CS) teachers from primary schools. Data collected from ethnographic observation, teacher interviews, and artifacts, were analyzed using open coding and triangulation rooted in the ethnographic, interpretivist approach. Results: Three sets of external influences were found salient for teachers' curriculum decisions, including the orientation of state-level educational policies, AI faculty at a partner university, and students' media and technology environments. The teachers' situational logics and strategic actions were reconstructed with thick descriptions to uncover how they navigated and negotiated the external influences to fulfill local challenges and expectations in classrooms and schools. Conclusions: The ethnographic study uncovered the dynamic and multifaceted negotiation involved in the collaborative curriculum development, and offers insights to inform policymaking, teacher education, and student support in engineering education.

Gross ecosystem product (GEP) is the aggregate economic value of the final goods and services of the ecosystem produced within a given area, providing a potentially effective means of linking nature protection and restoration to development.In this study, we calculated the values and distribution of biodiversity, GEP and gross domestic product (GDP) in the 547 counties of Southwestern China, a biodiversity hotspot. We then divided the region into nine types of area based on spatial correlations between the three indicators.Areas with high biodiversity, GEP per km2 and GDP per capita were mainly found in and around the large cities of Kunming, Chengdu and Guiyang, reflecting a reasonably high degree of coupling coordination between ecosystems and the local economy.Contrary to our hypotheses, areas with high biodiversity do not necessarily have high GEP, and regions with high GDP do not inherently have low biodiversity. This suggests that synergies exist between biodiversity conservation and economic development, and that increasing the value of material and non‐material services in GEP could improve this alignment.Based on our findings, we argue that GEP can act as a bridge between biodiversity conservation and economic development. Understanding the interactions between biodiversity, GEP and GDP can inform the design of effective policies. In particular, we suggest that sustainable agricultural intensification and forestry along with ecotourism offer promising pathways for advancing nature‐positive development in this ecologically critical region. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

With the serious pollution of water resources in the process of social development, green and environmentally friendly photocatalytic technology has gradually attracted widespread attention. Though photocatalytic theory and technology are relatively mature, the current research is mainly focused on powders and less on the photocatalytic membranes, and few real practical cases of photocatalysis are studied. To summarize and update these advanced studies, this paper introduces the latest progress in photocatalytic membranes including discussing the differences of photocatalytic mechanism between free and fixed photocatalytic systems and the design philosophy of photocatalytic membranes. At the same time, some common fabrication methods, including in situ growth, coating, spinning, casting, and foaming methods are analyzed. Besides, the photocatalytic performances of different photocatalytic membranes in the actual environment and their use conditions are explored. Finally, a critical view of future trends is included. The explanation of the photocatalytic mechanism, large‐scale production, and the practicality of photocatalytic products are all problems that need to be solved in the future. [ABSTRACT FROM AUTHOR]

While AI has become more prevalent in our society than ever, many young learners are found holding various naive, erroneous conceptions of AI due to the influence of their technology and media environments. To address this issue, this study seeks to propose a novel pedagogical solution to improve upper‐elementary school students' scientific understanding of AI. Following a theory‐informed design convention, we propose an embodied, analogical and disruptive (EAD) approach which is centred on a human–AI comparison through analogical teaching and embodied interaction. To evaluate the impact of this approach, a matched‐group experimental study with pre‐ and posttest interviews was conducted among 77 Grade 6 elementary students in China. The statistical analysis showed that the experimental group, learning via the EAD approach, significantly outperformed their counterparts receiving direct instruction in terms of student growth in understanding. Qualitative analyses revealed the strengths of the EAD approach in supporting student engagement, abstract thinking and system thinking, and its limitations in cognitive overload and communication issues. Patterns of student learning were identified, including their mental schemas and strategies. The EAD approach, as an evidence‐based, age‐appropriate pedagogical solution, demonstrates the value of embodied cognition and human–AI analogy in AI education in elementary education. Practitioner notesWhat is already known about this topic Many young students hold naive, erroneous conceptions about AI.Pedagogical design has long been a critical challenge in teaching AI in K‐12 schools.Human–AI analogies are often used to teach AI, but with obvious pitfalls to be fixed.What this paper adds An embodied, analogical and disruptive (EAD) approach is proposed to improve students' understandings of AI.An experimental study shows statistically significant greater gains in students' understanding with the EAD approach than with the conventional AI‐only direct instruction approach.Qualitative analysis shows the strength of the EAD approach in supporting student engagement, abstract thinking and systems thinking, and its limitations in cognitive overload and communication issues.Implications for practice and/or policy The EAD approach offers a novel pedagogical solution to promote inclusive and quality AI education for young learners.The EAD approach highlights the pedagogical value of embodied cognition and human–AI analogy in AI education.The EAD approach can complement programming and other plugged activities in AI, and more broadly technology and engineering education. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to identify molecular mechanisms in brain tissue of Rasmussen encephalitis (RE) when compared to people with non‐RE epilepsy (PWE) and control cases using whole exome sequencing (WES), RNAseq, and proteomics. Methods: Frozen brain tissue (ages = 2–19 years) was obtained from control autopsy (n = 14), surgical PWE (n = 10), and surgical RE cases (n = 27). We evaluated WES variants in RE associated with epilepsy, seizures, RE, and human leukocyte antigens (HLAs). Differential expression was evaluated by RNAseq (adjusted p <.05) and label‐free quantitative mass spectrometry (false discovery rate < 5%) in the three groups. Results: WES revealed no common pathogenic variants in RE, but several rare and likely deleterious variants of unknown significance (VUS; ANGPTL7/MTOR, SCN1A, FCGR3B, MTOR) and more common HLA VUS in >25% of RE cases (HLA‐DRB1, HLA‐DQA2), all with allele frequency < 5% in the general population. RNAseq in RE versus PWE (1516 altered transcripts) revealed significant activation of crosstalk between dendritic and natural killer cells (p = 7.94 × 10−6, z = 2.65), in RE versus control (7466 transcripts) neuroinflammation signaling activation (p = 6.31 × 10−13, z = 5.07), and in PWE versus control (945 transcripts) phagosome formation activation (p = 2.00 × 10−13, z = 5.61). Proteomics detected fewer altered targets. Significance: In RE, we identified activated immune signaling pathways and immune cell type annotation enrichment that suggest roles of the innate and adaptive immune responses, as well as HLA variants that may increase vulnerability to RE. Follow‐up studies could evaluate cell type density and subregional localization associated with top targets, clinical history (neuropathology, disease duration), and whether modulating crosstalk between dendritic and natural killer cells may limit disease progression. [ABSTRACT FROM AUTHOR]