From entertainment to medicine and engineering, artificial intelligence (AI) is now being used in a wide range of fields, yet the extent to which AI can be effectively applied to the creative arts remains to be seen. In this research, a neural algorithm of artistic style was used to generate six AI paintings and these were compared with six paintings on the same theme by an amateur painter. Two sets of paintings were compared by 380 participants, 70 percent of whom had previous painting experience. Results indicate that color and line are the key elements of aesthetic appreciation. Additionally, the style transfer had a marked effect on the viewer when there was a close correspondence between the painting and the style transfer but not when there was little correspondence, indicating that AI is of limited effectiveness in modifying an existing style. Although the use of neural networks simulating human learning has come a long way in narrowing the gap between paintings produced by AI and those produced in the traditional fashion, there remains a fundamental difference in terms of aesthetic appreciation since paintings generated by AI are based on technology, while those produced by humans are based on emotion.

Despite the increasing adoption of generative artificial intelligence (GenAI) to facilitate second language (L2) writing, current reviews are insufficient in their depth and scope to effectively highlight the forefront of research trends. To bridge the gap, this paper synthesizes findings from 73 empirical studies on GenAI-assisted L2 writing based on the six dimensions of the revised technology-based learning model, including theories, GenAI, participants, objectives, methods, and outcomes. Results reveal that (1) Sociocultural, cognitive, and sociocognitive theories are most frequently used. (2) ChatGPT is the most widely used GenAI model, and “defining objectives + refining outputs” is one of the most common prompt engineering strategies. (3) GenAI primarily assists learners of English as a second language (ESL) across various proficiency levels. (4) Major objectives include learner perceptions (attitudes, perceived usefulness) and academic writing practices. (5) Researchers prefer mixed-methods and qualitative studies over quantitative ones, focusing on content, organization, grammar, and mechanics as primary writing metrics. (6) Affordances (feedback provision, content development) and challenges (learner dependency, inconsistent feedback) coexist, with studies generally reporting positive and mixed effects with moderate and large effect sizes. It highlights prospects for writer-friendly GenAI platforms and enhanced GenAI competence and pedagogy while identifying crucial research directions.

Taiwan’s indigenous communities have an abundance of unique cultures. Their service industries with local and foreign cultures have opened up distinct opportunities for sustainable development. Despite the enormous potential of aboriginal communities, particular attention should be given to ecology and sustainability. The traditional emphasis on craftsmanship and design is shifting to a new focus on the service industries and experimental design, which is not limited to the design of tangible products. Design concepts are now being applied to service industries that span several fields and are also being used to come up with systematic solutions for real-life problems. However, in the service industry, design experience must be used when introducing design concepts. The problem is how to shift from “High-tech” to “High-touch”, for the aborigines are used to designing products at the usability level. This research proposes a model of experience design for use in aboriginal culture revitalization. Three different cases show how to apply the framework from experience design to local revitalization. Results show that the model can integrate the principles of sustainability into service industries and that it needs to be verified in future studies.