The integration of generative AI into programming education has produced a widely reported tension between performance and learning. We distinguish immedia

With the increasing digitization of healthcare, WeChat has emerged as a popular platform for delivering prenatal education in China. While studies have shown its potential benefits, few investigations systematically explore how nurses implement WeChat-based prenatal education and how pregnant women engage with these interventions within public hospital settings. Understanding both provider and patient perspectives is critical for developing equitable, effective digital maternal care. A qualitative exploratory design was employed in four public hospitals in Shanxi Province, China. Seventeen obstetric nurses and eight pregnant women participated in semi-structured interviews between March and August 2024. Data were analyzed using thematic analysis to identify commonalities and divergences between nurse-driven and patient-driven experiences. Nurses perceived WeChat as extending their professional reach beyond physical clinic hours and enhancing patient education, aligning with the Technology Acceptance Model constructs of perceived usefulness and ease of use. However, organizational constraints, digital training gaps, and blurred work–life boundaries posed challenges. Pregnant women similarly recognized WeChat’s convenience for quick clarifications and access to nurse-vetted information, yet wide variations in digital health literacy led to uneven engagement. Cultural factors, such as extended family involvement and preferences for in-person consultations, further influenced both nurse and patient usage patterns. Despite these challenges, participants converged on WeChat’s value as a complementary platform that could reduce clinic visits, offer immediate reassurance, and bolster continuity of care. WeChat-based prenatal education holds significant promise for improving maternal health outcomes by facilitating frequent, real-time communication between nurses and expectant mothers. Nonetheless, the findings underscore the need for hospital-level policies, structured training programs, and culturally sensitive strategies to address privacy concerns, manage workloads, and reduce digital disparities. An integrated approach that combines nurse acceptance and patient digital literacy is essential to fully harness WeChat’s potential in public hospital contexts. The study highlights practical steps for enhancing digital prenatal interventions, including standardized protocols for WeChat-based interactions, comprehensive nurse training in eHealth communication, and tailored support for pregnant women with limited digital skills. Such interventions can inform wider policymaking on telehealth and guide technology developers in creating user-friendly, secure digital platforms that optimize maternal care and reduce health inequities. Not applicable.

Human–AI collaboration is increasingly integral to a variety of domains where creative ideation unfolds in iterative cycles, yet most existing studies evaluate AI-generated concepts in a single step. This paper addresses the gap by investigating “Creative Momentum Transfer”—how the timing (early vs. late) and labeling (AI-labeled vs. unlabeled) of AI prompts shape multi-round human ideation. In a between-subjects experiment (N = 247), participants proposed solutions for plastic pollution over two rounds, with AI suggestions introduced either at the outset or mid-process and labeled explicitly or not. Results reveal that early AI prompts increase overall creativity but induce stronger anchoring, whereas late AI prompts trigger a mid-round pivot that fosters more divergent thinking yet still boosts final outcomes compared to a no-AI control. Labeling amplifies both subjective and objective adoption of AI ideas, although most participants could detect AI sources even when unlabeled. Furthermore, qualitative interviews highlight nuanced perspectives on perceived ownership, authenticity, and the ways in which labeling triggers deeper scrutiny of the AI’s style. By demonstrating that baseline creativity moderates these effects more robustly than trust in AI, this study advances our theoretical understanding of multi-round human–AI synergy while offering design guidelines for next-generation creativity support systems. We discuss how user-centered design can balance rapid convergence (via early AI) with strategic pivot opportunities (via late AI) and weigh transparent labeling against ethical considerations of authorship and user autonomy.