This study investigates how semester-long and framework-guided engagement with Lesson Play—scripting hypothetical teacher-student interactions—affects preservice mathematics teachers’ professional growth. Twenty-five preservice teachers collaboratively designed, analyzed, and revised Lesson Plays for three instructional situations over one semester. Their work was guided by the Teacher Moves for Supporting Student Reasoning (TMSSR) framework, which categorizes instructional actions as Eliciting, Responding, Facilitating, and Extending student reasoning along a continuum from low- to high-potential. Analysis reveals two key findings. First, preservice teachers’ instructional design skills improved, evolving from teacher-directed and procedural plays to sophisticated and inquiry-oriented designs featuring high-potential moves, particularly in the Extending category. Second, alignment between preservice teachers’ collaborative planning dialogues and their written Lesson Plays strengthened substantially, demonstrating improved ability to operationalize complex pedagogical ideas into executable instructional designs. These findings suggest that sustained and semester-long engagement combining Lesson Play’s cognitive rehearsal with the TMSSR framework’s structured analytic lens creates a powerful mechanism for integrated professional growth, simultaneously enhancing instructional design skills and fostering metacognitive alignment between pedagogical thought and instructional design.
This article examines how international events are institutionally framed as instruments of city-level public diplomacy. While existing research has explored city diplomacy, place branding, and international events, less attention has been paid to the institutional mechanisms through which event-based public diplomacy is structured, particularly in non-Western contexts. Adopting an institutional perspective on city public diplomacy, the study analyses publicly available bidding frameworks, national bidding support manuals, and official city-level strategic documents related to international events in the Republic of Korea. Using qualitative document analysis and thematic analysis, the article identifies three recurring diplomatic logics: capacity-oriented, value-oriented, and network-oriented public diplomacy. These logics show how event-related policy texts frame hosting not simply as a promotional opportunity, but as a means through which cities may demonstrate governance competence, articulate normative commitments, and build transnational relationships. By foregrounding institutional texts rather than post-event outcomes, the study suggests that international events may function as institutionalised platforms for city public diplomacy and contributes to place branding and public diplomacy scholarship in a non-Western governance context.
This study investigates how abstract-level discourse features relate to citation impact when considered alongside topical structure. A harmonized set of 436 English-language articles and reviews (1995-2024) from the Web of Science (Linguistics, Language & Linguistics, Education & Educational Research) was analyzed. Topic structure was mapped using bibliometrix through the Biblioshiny interface with all keywords (DE + ID; minimum frequency = 5), producing a co-word network, a thematic map (centrality & times; density), and period-wise evolution. Discourse was profiled with five Coh-Metrix composites-narrativity, syntactic simplicity, concreteness, referential cohesion, and deep cohesion-standardized as z-scores. Citation impact was normalized as average citations per year (TCperYear). Annual production peaked in 2003-2006, whereas TCperYear peaked later, indicating lagged diffusion. Thematic mapping showed a Motor axis centered on language-meaning-cognition, a Basic infrastructure of corpus/syntax/English, and bridging roles for computational linguistics and machine learning. In OLS with HC3 errors, syntactic simplicity and referential cohesion had small but significant negative associations with TCperYear; other indices were not significant. Results were robust in auxiliary checks and showed low multicollinearity (all VIFs < 2). Findings suggest that authors should maintain information density, limit mechanical repetition, and use only as much syntactic complexity as needed for precision-especially in high-centrality topics. (Silla University)
In recent years, China's rapid advancements in artificial intelligence (AI) technology have positioned it as a global leader in information processing, intelligent chip development, and deep learning technologies. The pervasive impact of AI has led to transformative shifts in various domains of life, with its integration across industries substantially enhancing overall production efficiency. Particularly evident in product design, the infusion of AI technology has catalyzed significant productivity improvements. This research investigates the amalgamation of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) within the framework of AI technology to establish a robust product design system. The system aims to enhance the efficiency and efficacy of design processes by leveraging the complementary strengths of GA and PSO. This integration has propelled product design into accelerated evolution and modernization. The study delves into AI's application of deep neural networks in product design within the context of the digital age. Artificial intelligence technology is used in product design in the context of the Internet. First, the concept of AI technology is briefly introduced, and DNN algorithms are described in detail, including the PSO and Genetic Algorithms. Second, the product design and development process based on artificial intelligence technology, design guidelines, and technical features are mainly described. Finally, using the seat as the research object, the application effect of artificial intelligence technology in product design is analyzed, and 10 subjective evaluation questions are set to experiment. Regarding material texture, the highest score for the B3 Barcelona chair is 4.3. Users prefer leather seats with diverse materials, and the score for painted wood materials is lower. From the product structure and shape analysis, the highest score is the B3 Barcelona chair, which has a value of 4.1, and the lowest score is the B1 red and blue chair, which has a value of 3.0. Based on this result, users like arc and curve shape much more than straight structures.
This study employs a mixed-methods design - two 2 x 2 between- subjects experiments and a PLS-SEM analysis (N = 639) to examine the dual pathways driving user self-disclosure in Human-AI co-creation. Results indicate that Self-congruence and AI attachment not only directly promote disclosure intention but also exert an indirect mediating effect by enhancing perceived value and reducing perceived privacy risks. The study further found that self-congruence has an inverted U-shaped effect on perceived value, and that privacy self-efficacy positively moderates the relationship between self-congruence/AI attachment and perceived value, while negatively moderating the relationship between self-congruence/AI attachment and perceived privacy risk. This study integrates Privacy Calculus, Self-congruence, and Atachment Theory in Human-AI co-creation scenarios, revealing the "over-personalization paradox" and deepening our understanding of privacy tradeoffs in Human-AI collaboration. It also provides empirical evidence and practical implications for personalized adaptation, the establishment of user trust, and privacy feature development in AI collaborative design.
