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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.

Learning mathematics has been considered as a great challenge for many students. The advancement of computer technologies, in particular, artificial intelligence (AI), provides an opportunity to cope with this problem by diagnosing individual students’ learning problems and providing personalized supports to maximize their learning performances in mathematics courses. However, there is a lack of reviews from diverse perspectives to help researchers, especially novices, gain a whole picture of the research of AI in mathematics education. To this end, this research aims to conduct a bibliometric mapping analysis and systematic review to explore the role and research trends of AI in mathematics education by searching for the relevant articles published in the quality journals indexed by the Social Sciences Citation Index (SSCI) from the Web of Science (WOS) database. Moreover, by referring to the technology-based learning model, several dimensions of AI in mathematics education research, such as the application domains, participants, research methods, adopted technologies, research issues and the roles of AI as well as the citation and co-citation relationships, are taken into account. Accordingly, the advancements of AI in mathematics education research are reported, and potential research topics for future research are recommended.

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.

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.

This study guided 97 undergraduates using generative artificial intelligence (GenAI) to conduct multimodal digital storytelling (M-DST) learning activities. Furthermore, the study examined the differences in M-DST ability and critical thinking awareness among undergraduates with different levels of learning motivation and their perceptions of this learning approach. The study demonstrated that the high learning motivation level (HLM) group exhibited significantly superior M-DST performance compared to the low learning motivation level (LLM) group, particularly regarding story structure, accuracy, completeness, appearance, creativity, and interactivity. Furthermore, the HLM group demonstrated a significantly higher level of critical thinking awareness than the LLM group. Regarding learning attitudes, the HLM group demonstrated a greater interest in and receptivity of using GenAI for artistic creation, and a higher curiosity about course satisfaction and AI technology. Irrespective of learning motivation, undergraduates rated GenAI and traditional digital storytelling as being similarly interesting. Nevertheless, some undergraduates stated their concerns regarding the possibility of over-reliance on AI, which might lead to poor learning outcomes or even ethical problems. Some of them highlighted the problems encountered when using AI, such as inaccurate information and challenges in expressing emotions. Despite these issues, most of them considered that mastering AI technology as well as critical thinking and creativity are essential competences nowadays. They also emphasized the importance of collaborating with AI to complete tasks in a more efficient and effective manner. Based on these findings, several recommendations and some guidance are provided to educators for designing and implementing GenAI-based teaching and learning activities.

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.

Collaborative argumentation allows groups to express, criticize, and integrate arguments to achieve the co-construction of collective knowledge. However, students often face challenges when proposing diversified arguments, gathering evidence, and rebutting others reasonably. Incorporating generative conversational agents (GCAs) into collaborative argumentation has been demonstrated to effectively broaden the perspective of the argument and to stimulate the generation of new ideas. For this study, we designed rhetorical argumentation customized strategies (RACS), dialectical argumentation customized strategies (DACS) for collaborative argumentation, and a mixed-strategy (RACS+DACS), and compared their effects on the quality of argumentation mappings and argumentation discourse patterns. A total of 121 first-year postgraduate students were enrolled: 33 for the control group, 33 for the RACS group, 27 for the DACS group, and 28 for the mixed-strategy group. Results found that: (1) Regarding the quality of argumentation mappings, DACS could help students select high-quality evidence and learn the logical skills from evidence reasoning to claims. In addition, the mixed-strategy could help students search for multiple types of evidence to support their position; (2) Regarding argumentation discourse patterns, in the characteristics of the structural dimension, DACS could help students use evidence to support higher-order claims during argumentation. The mixed-strategy could help students use evidence to rebut others' arguments in group discourses. However, no significant differences were detected among the four groups in the characteristics of the social dimension.

As is indicated by the United Nation’s Sustainable Development Goal 4, it is crucial to have access to inclusive and quality education for all. For English as a Foreign Language (EFL) learners, reading English is a basic skill for learners to acquire and exchange information and to have lifelong learning experiences. To provide a vivid EFL learning environment, a visual prompt scaffolding-based VR (VPS-VR) approach was proposed to enhance students’ reading comprehension skills. To investigate the effectiveness of the proposed approach, an experiment was conducted in an English reading course at a Chinese university. Students from experimental group A (N = 31) learned with the VPS-VR approach, experimental group B (N = 32) learned with the virtual reality (VR) approach, and the control group learned with the traditional instruction (TI) approach. The results revealed the positive effects of the VPS-VR approach on students’ EFL reading comprehension, learning motivation, and English learning anxiety. Furthermore, it was also found that experimental students’ lower-level skills of reading comprehension, such as information location and text comprehension, were significantly improved, rather than the higher-level skills of reflection and evaluation. Fifteen students participated in interviews, and their learning experience and technology acceptance are also discussed.

Writing is a challenging task for English Foreign Language (EFL) instruction. Based on artificial intelligence technology, Automatic Writing Evaluation (AWE) has received considerable attention from the EFL research community in recent years, since it can provide timely and personalized feedback to EFL writing learners. However, researchers have pointed out that while AWE can provide satisfactory feedback on vocabulary use and grammar, it is relatively inadequate at providing efficient feedback on organization, coherence, and content. Spherical Video-based Virtual Reality (SVVR) can provide a highly immersive and in-depth interaction learning environment that makes up for this shortcoming. Authentic experiences help enhance EFL writing learners’ perceptions and understanding of context, and assist students in creating constructive internal connections between their personal experiences and the topic of their writing, thus improving their writing quality. Therefore, the current study proposed an approach which integrated SVVR and AWE to investigate its effects on EFL writing. To investigate the effectiveness of the proposed approach, a quasi-experiment was carried out in a university’s EFL writing course. The experimental group (37 students) used the SVVR–AWE approach, while the control group (39 students) used the conventional approach with AWE. The results showed that the learning method not only considerably enhanced the students’ EFL writing performance, but also raised their motivation, self-efficacy, and sense of presence, as well as reduced their EFL writing anxiety. Furthermore, interviews were performed and a thematic inductive qualitative analysis of the interview data was conducted to investigate the impact of this learning method on students’ learning behaviors and perceptions.

In traditional collaborative argumentation activities, students often struggle to present arguments from diverse perspectives. ChatGPT is capable of understanding user prompts and generating corresponding responses, and it can play different roles with diverse backgrounds to argue with students, creating the possibility of promoting the quality of their argumentation. However, to make ChatGPT's responses work well for argumentation, students need to give appropriate prompts. Therefore, this study proposed the role-playing prompt-based ChatGPT-assisted Collaborative Argumentation (CaCA) approach, and a quasi-experiment was conducted to examine its effects on students' argumentation outcomes, processes, and perceptions. Sixty-six first-year graduate students engaged in this experiment: the experimental group adopted the role-playing prompt-based CaCA approach, while the control group adopted the conventional CaCA approach. Results showed that the role-playing prompt-based CaCA approach broadened students' perspectives in their arguments and increased the connections between data and claims, forming the chain of arguments centered on warrant and backing in their discourse. However, it did not significantly enhance their ability to edit ideas deeply or increase their willingness to give rebuttals. This research provides new insights into the application of ChatGPT in a micro-level collaborative argumentation context.