Kai-Yu Tang

In higher education, agentic AI goes beyond drafting content to creating learning management system (LMS) artefacts and scheduling releases under teacher oversight. Adoption research has modelled intention towards tools that draft rather than act, leaving delegation to a system that acts unobserved. This study estimated the effect of assignment to an action-taking scenario on teachers’ workflow choices, using Rogers’ Diffusion of Innovations (DOI) theory to explain delegation within that scenario. We randomly assigned 290 university teachers to a GenAI drafting-only condition (n = 140) or an LMS-integrated agentic condition (n = 150); each made step-by-step choices in a scenario-based six-step quiz workflow combining content drafting with student-facing platform actions. Teachers assigned to the agentic scenario chose AI support for more steps (d = 0.65), but the gain sat almost entirely on content drafting (d = 0.99) and was negligible for platform actions (d = 0.12). This content–platform boundary was sharp: 92% chose to delegate question writing, while 77% chose to schedule releases manually. Most chose limited autonomy (48% selected draft-only, 35% selected approval-gated publishing, 5% selected auto-publish). The five DOI attributes and controls explained substantial variance in an Agentic Use Behaviour Index (AUBI) of delegation, autonomy, and permissions (R² = 0.65). Complexity, understood as oversight burden, was negatively associated with AUBI, and its association with non-manual engagement was stronger in the agentic scenario; observability was not significant. The direction of the AUBI results held across four weightings and count and ordinal models, and exploratory clustering suggested four adopter profiles forming a permission ladder. Extending DOI from tool adoption to task delegation, the results favour draft-first defaults, approval gates, and staged permissions, with field replication using LMS logs a priority.

Agentic artificial intelligence (AAI) systems—capable of autonomously executing multi-step instructional tasks with minimal human oversight—are reshaping higher education while exposing the limitations of adoption- and continuance-centred models that treat value and risk as independent predictors. In an environment of widespread AI availability, the more meaningful question concerns how deeply educators integrate delegation-capable systems into teaching, assessment, and instructional workflows. Drawing on affordance theory, institutional theory, and automation risk perspectives, this study develops and tests a dual-pathway framework that conceptualises pedagogical integration as a dynamic value–risk trade-off embedded within institutional and contextual conditions. Survey data from 338 higher-education educators across Global North and South contexts, all of whom underwent a structured screening process to verify exposure to agentic rather than conventional generative AI systems, were analysed using PLS-SEM with robustness and endogeneity checks. The model explains substantial variance in pedagogical integration (R² = 0.619), pedagogical value (R² = 0.621), and autonomy risk (R² = 0.615). Institutional AI capability is positively associated with pedagogical value and negatively associated with autonomy risk, whereas perceived AI complexity shows the opposite pattern. Higher pedagogical value is also associated with lower perceived autonomy risk (β = −0.445, p < 0.001), suggesting that educators may interpret system autonomy as a manageable pedagogical feature rather than solely as a source of concern. Regulatory clarity weakens the complexity–risk relationship, while AI legitimacy strengthens the value–integration relationship. Given the cross-sectional design, findings should be interpreted as associative rather than causal.

With the rapid growth of the global cruise tourism industry and its increasing environmental impact, there is an urgent need to address sustainability challenges in line with the SDGs, especially in Taiwan. Despite the growing research on environmental education, there is a lack of a theoretical framework from the perspective of the stimulus-organism-response (S-O-R) paradigm that examines the relationships between external stimuli, internal organisms, and individual responses to environmental education in the context of cruise tourism. The proposed framework includes attention to environmental issues and awareness of consequences as external stimuli. These stimuli influence affective and cognitive processes, which are internal states of the organism. In turn, the affective and cognitive states drive pro-environmental behavioral responses. Additionally, the proposed framework incorporates two potential moderating factors: cultural differences and environmental education with emerging technologies. Implications for environmental education in cruise tourism are provided.

Past literature has advocated the integration of channels through the offline-to-online and online-to-offline models. However, little research has investigated the interrelationships effects between the two channels. Drawing on the literature from self-service technology (SST) and expectation–confirmation theory, this study aims to investigate key attributes of SST and assess their impact on consumer evaluations across offline-to-online and online-to-offline channels. A questionnaire survey was administered at convenience stores and 360 user responses were collected through a physical self-service kiosk. Two-stage structural equation modeling with linear structural relations (LISREL version 8.54) software was used for data analysis. The empirical results verified the attributes of physical SST (physical system’s service quality and perceived convenience) and online SST (virtual system’s service quality and perceived ubiquity) as critical antecedents of satisfaction and attitude and the subsequent behavioral intentions toward each channel. However, some transitional effects from offline (physical kiosk use) to online (kiosk app adoption) intention were not as significant as hypothesized. The offline attributes of perceived convenience and satisfaction had no significant impact on online SST significantly (kiosk app), except for the physical system’s service quality. Discussions and implications are provided, including strategies for concise functional design and essential SST services.

This study examines the role of Artificial Intelligence (AI) in enhancing personalization, analyzing information dynamics, and developing scalable methodologies within Online Social Networks and Media (OSNEM), with a focus on user protection. Through a systematic review using the PRISMA framework and BERTopic modeling, key AI applications in OSNEM were identified, including fake news detection, sentiment analysis, hate speech detection, big data analysis, bot detection, and insights into public health, disaster relief, and mental health. Although AI techniques and multimodal frameworks have significantly improved content personalization, challenges like algorithmic bias and echo chambers remain. To address these, the implementation of fairness-aware learning models is recommended to ensure personalization stays ethical. Advanced AI techniques, such as Dynamic Memory Networks and Temporal Convolutional Networks, have shown strong capabilities in tracking opinion dynamics and combating misinformation. Additionally, Generative AI offers opportunities for content creation but also raises concerns about misinformation, requiring robust moderation frameworks. Emerging technologies like Artificial Real Intelligence (ARI), which simulate human reasoning and decision-making, could further improve the management of complex online interactions. The study highlights the need for scalable AI methodologies, such as multitask learning frameworks, to efficiently handle the vast amounts of real-time data generated by social media while addressing cross-platform adaptability and computational efficiency.