Mobile devices are becoming ubiquitous methodologies and tools, providing application for learning and teaching field. On the basis of the widespread use of wireless devices and mobile computing technology, this study proposes a context-aware plant ecology learning system (CAPELS) based on context-aware technology; adapting deep neural networks (DNN) and leaf vein and shape identification algorithm which can identify plant leaves, this system automatically provides relevant botanical and growth environment knowledge to the learners. Therefore, during outdoor education, it can assist learners in accurately obtaining the required relevant botanical and growth environment knowledge. The experimental results indicate that students who used CAPELS performed better learning about plant ecology than those who did not. We also delivered questionnaires to those who used CAPELS and analyzed the results by using the partial least squares (PLS) method. The results have shown that CAPELS can encourage student’s learning motivation and thus improve their learning effectiveness. Thus, CAPELS provides a new educational platform for promoting ecology learning approach and effectively improves student learning efficiency and motivation.

Previous research has shown that although military personnel are at high risk of developing mental disorders because of the excessive stress caused by their work, they also display low levels of intention to seek assistance because of the military culture. This, in turn, creates exorbitant costs for their respective countries. With the rapid development of artificial intelligence (AI)-related digital technologies, chatbots have been successfully applied to mental health services. Although the introduction of chatbots into the military to assist with mental health services is not common, it may become a future trend. This study aims to construct the critical factors for introducing chatbots into mental health services in the military, the relationships between the effects, and a weighting system, to ensure that the introduction of chatbots complies with sustainable practices. This includes four stages. In the initial stage, in accordance with the AI-readiness framework, in combination with the findings of previous research and specialist recommendations, preliminary indicators and items were developed. In the second stage, Fuzzy Delphi was used to confirm each dimension and indicator. In the third stage, using DEMATEL, an influential-network-relation map (INRM) of dimensions and indicators was created. In the fourth stage, using DANP, the relationships between the effects of the indicators and the weighting system were established. The findings of this study indicated that: (1) the key to success includes four dimensions and twenty-one indicators; (2) there is an interdependent relationship between the four dimensions and twenty-one indicators, and they influence each other; and (3) the four dimensions are technologies, goals, boundaries, and activities, in order of importance. Finally, specific suggestions are put forward to provide references for future practical applications and research, drawing on the results of this research.