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This study aimed to determine the perceptions and attitudes of preservice teachers of Chinese as a foreign language (CFL) toward applying information and computer technology (ICT) and image-generative artificial intelligence (image-GAI) tools in CFL teaching from the perspective of drawings. This 2-week study involved 20 preservice CFL teachers from a university in northern Taiwan. Various data were collected, including questionnaire responses and drawings (both hand-drawn and artificial-intelligence-assisted) by preservice teachers. The collected data were analyzed to determine how the experience of using image-GAI tools influenced how preservice CFL teachers perceive ICT-assisted CFL teaching, and how they perceive the potential of image-GAI tools in enhancing their creativity. The results indicated that most conceptions held by the preservice CFL teachers of ICT-assisted Chinese language teaching involved teachers and learners as the primary people involved, and that they tended to adopt a teacher-centered teaching mode. The preservice CFL teachers had positive attitudes toward using image-GAI tools in CFL teaching. However, they were more reserved regarding whether generative AI tools can promote diversity in CFL teaching and enhance the creativity of preservice teachers. The most common elements in the drawings by the preservice teachers were the activities and the people involved in CFL teaching. Two other noteworthy findings were (1) most preservice CFL teachers believed that this new technology (image-GAI tools) better reflected their understanding of ICT and CFL teaching, and (2) that their drawings indicated that having experience of using image-GAI tools influenced their perception of ICT and CFL teaching. [ABSTRACT FROM AUTHOR]

As technology-enhanced children’s learning has gained wide attention, programmable robots have been gradually introduced in early childhood education. Hence, it would be valuable to understand how young children perceive robot programming learning. Draw-a-picture technique is an ideal method to elicit ideas, thoughts, and feelings for children with limited literacy, and epistemic network analysis (ENA) is a novel analytical method to analyze children’s conceptions through the visualized network model. Therefore, this study employed a draw-a-picture technique and ENA to explore 189 5-6-year-old young children’s conceptions of robot programming learning and probe whether their conceptions differ by gender and learning achievements. Results revealed that most children believed that with robot programming kits, they could engage in programming activities with peers in any location and held positive emotions and attitudes. In addition, young children’s conceptions of robot programming learning differ notably by gender and learning achievements. Based on the current findings, several suggestions were proposed, which could set a reference for future robot programming teaching in early childhood education. [ABSTRACT FROM AUTHOR]

With the rapid development of generative artificial intelligence (GAI), the performance and usability of related tools, such as ChatGPT, have significantly improved. The advancement has fostered researchers to increasingly focus on students' perceptions and application of the roles, functionalities, and interaction patterns of these tools in higher education. The present study adopted the draw-a-picture technique to explore the viewpoints and conceptions of undergraduates with different growth mindsets regarding the roles and functionalities of ChatGPT in learning. It also analyzed their interaction process with ChatGPT, especially their interaction skills and question types. The results showed that there were significant differences in the conceptions of "locations," "learning content," and "learning activities" of students with different growth mindsets. In the interaction process between undergraduates and ChatGPT, significant differences existed in the interaction skills and question types of students with different growth mindsets. Besides, students with different growth mindsets also had different learning achievements and critical thinking tendencies. The findings revealed the conceptions of students with different growth mindsets regarding the roles and functionalities of ChatGPT in learning, and also provided valuable insights for teachers. These findings are beneficial for educators to more accurately adjust and optimize the application of these tools in teaching activities based on students' different growth mindsets. [ABSTRACT FROM AUTHOR]

This study investigates the roles, applications, and trends of concept mapsupported education (CMSE) by reviewing the studies published from 1992 to 2020. Several dimensions, such as the media of concept maps, roles of concepts, sample groups, research methods, application domains, learning contexts, research foci, as well as the citation and cocitation relationships, are taken into account based on the technologybased learning model. From the review results, two thematic clusters were found, namely concept map-facilitated knowledge construction in collaborative learning, and concept map-based learning design to improve learning achievement and motivation. The research focused more on the applications in engineering courses, followed by science and language courses, and potential was also found in arts, business or management, and mathematics. Learning contexts for CMSE are mainly in-class activities, followed by online discussion activities; moreover, it was found that the number of applications in digital game-based learning contexts is increasing. In terms of research foci, more studies investigated learners' performance, attitude/motivation, and perceptions, while less emphasis was put on developing learners' skills, higher order thinking, as well as collaboration and communication competences. Accordingly, several suggestions for future research of CMSE are provided as a reference for researchers and policy makers. [ABSTRACT FROM AUTHOR]

Automated writing feedback supported by artificial intelligence (AI) techniques has attracted the attention of English as Foreign Language (EFL) researchers. However, there is insufficient evidence and inconsistent conclusions on the actual impacts of AI on students' writing skills. In addition, in the field of EFL writing, there are few studies which have integrated appropriate teaching strategies to promote the effectiveness of EFL writing teaching and learning. Furthermore, it remains unclear how learners can effectively benefit from AI technology-based English writing. To solve this problem, this study proposed a reflective thinking promotion mechanism-based AIsupported English writing (RTP-AIEW) approach to deepen learners' thinking and improve their EFL writing quality. To investigate the effectiveness of this learning approach, a quasi-experiment was conducted in two EFL writing classes in a university. One class (50 students) was the experimental group learning with the proposed RTPAIEW approach, while the other class (53 students) was the control group learning with conventional AI-supported EFL writing. The results indicated that the proposed approach not only significantly improved the experimental group students' English writing performance, but also improved their self-efficacy and self-regulated learning, and significantly reduced their cognitive load. In addition, students' learning experience and perceptions are also discussed. [ABSTRACT FROM AUTHOR]

Owing to the rapid advancements in artificial intelligence (AI) technologies, there has been increasing concern about how to promote the use of AI technologies in school settings to enhance students' learning performance. Teachers' intention to adopt AI tools in their classes plays a crucial role in this regard. Therefore, it is important to explore factors affecting teachers' intention to incorporate AI technologies or applications into course designs in higher education. In this study, a structural equation modeling approach was employed to investigate teachers' continuance intention to teach with AI. In the proposed model, 10 hypotheses regarding anxiety (AN), self-efficacy (SE), attitude towards AI (ATU), perceived ease of use (PEU) and perceived usefulness (PU) were tested, and this study explored how these factors worked together to influence teachers' continuance intention. A total of 311 teachers in higher education participated in the study. Based on the SEM analytical results and the research model, the five endogenous constructs of PU, PEU, SN, and ATU explained 70.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effect of ATU was 0.793, followed by SE, with a total effect of 0.554. As a result, the intentions of teachers to learn to use AI-based applications in their teaching can be predicted by ATU, SE, PEU, PU and AN. Among them, teachers' SE positively influenced teachers' PEU and ATU towards adopting AI-based applications, and also influenced PU through PEU. In addition, the relationship between teachers' SE and AN was negatively correlated, which indicated that enhancing teachers' SE could reduce their AN towards using AI-based applications in their teaching. Accordingly, implications and suggestions for researchers and school teachers are provided. [ABSTRACT FROM AUTHOR]

Precision medicine has become an essential issue in the medical community as the quality of medical care is being emphasized nowadays. The technological data analysis and predictions made by Artificial Intelligence (AI) technologies have assisted medical staff in designing personalized medicine for patients, making AI technologies an important path to precision medicine. During the implementation of the new emerging technology, medical staff's learning intentions will have a great influence on its effectiveness. With reference to the Technology Acceptance Model, this study explored medical staff's attitudes, intentions, and relevant influencing factors in relation to AI application learning. A total of 285 valid questionnaires were collected. Five major factors, perceived usefulness (PU), perceived ease of use (PEU), subjective norms (SN), attitude towards AI use (ATU), and behavioral intention (BI), were used for analyzing the AI learning of medical staff in a hospital. Based on the SEM analytical results and the research model, the four endogenous constructs of PU, PEU, SN, and ATU explained 37.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effects of SN and PEU were 0.448 and 0.408 respectively, followed by PU, with a total effect of 0.244. As a result, the intentions of medical staff to learn to use AI applications to support precision medicine can be predicted by SN, PEU, PU, and ATU. Among them, subjective norms considering the influences of both supervisors and peers, such as encouragement, communication, and sharing, may assist precision education in supporting the learning attitudes and behavior regarding precision medicine. The research results can provide recommendations for examining medical staff's intention to use AI applications. [ABSTRACT FROM AUTHOR]