As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs.

The survivability of out-of-hospital cardiac arrest patients in China is notably low, at less than 1%, primarily due to difficulties in accessing nearby Automated External Defibrillator (AED) devices during emergencies. Therefore, the strategic deployment of AEDs is crucial for improving patient survival. This study adopts a novel hybrid methodology that integrates the Fuzzy Analytic Network Process (FANP) and Grey Relational Analysis (GRA) to assess and prioritize the critical factors for optimizing AED deployment in China. In the FANP model, the rate of cardiopulmonary resuscitation training among individuals in the area, the rate of AED training among individuals in the area, and records of AED device usage within the area are key factors for improving the effectiveness of AED deployment. Furthermore, in the GRA model, the factors with higher gray relational degrees are Visibility, Social Environment, and Accessibility. This analysis sheds light on which secondary indicators can have the most significant impact on improving specific primary indicators. The insights from this study can guide the optimization of AED deployment strategies in China and offer practical solutions for enhancing the deployment of relevant public medical facilities.

With the rapid advancement of AI in the education sector, Interactive AI Learning Environments (IAILE) have emerged as vital tools for facilitating personalized learning and enhancing learning outcomes. Trust in IAILE has become a key prerequisite for users’ continued engagement and adoption. Grounded in the Stereotype Content Model, this study develops a path model incorporating active and passive interaction to investigate how interaction experiences shape users’ social perceptions and trust, with user experience further examined as a moderating factor. Using PLS-SEM, data from 483 valid responses were analyzed. Results reveal that both active and passive interaction perceptions significantly enhance users’ social perception of system competence and warmth, which in turn positively affect trust in AI systems. Furthermore, novice users tend to rely more on perceived competence to build initial trust, whereas experienced users emphasize perceived warmth to maintain long-term trust. These findings underscore the importance of targeted interaction design and differentiated user experience strategies in cultivating trust, offering both theoretical insight and practical guidance for the development, design, and management of AI-based interactive learning systems.

With the rapid development of metaverse technologies in education, how teachers adapt to this emerging environment has become a critical issue. This study explores teachers’ emotional experiences and adaptation mechanisms within metaverse classroom. Grounded in awe experience theory and role theory, it examines how classroom features influence teachers’ role adaptation through both positive and negative awe experiences, thereby affecting teaching satisfaction and engagement. Using survey data from 692 primary and secondary teachers, Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) were conducted. Results show that the immersive, interactive, technically complex, and decentralized metaverse environments significantly enhance positive awe while unexpectedly reducing negative awe. These emotional responses improve teachers’ role clarity, leading to higher teaching satisfaction and engagement. Moreover, Multi-group analysis reveals stage-based differences: primary school teachers are more easily inspired by positive awe, whereas high school teachers demonstrate stronger technological adaptability and emotional regulation. This study not only expands the understanding of emotional mechanisms among teachers in educational technology research but also provides stage-specific and differentiated practical implications for teacher training and instructional design.