Explore the vast range of titles available.

Traditional nursing and health education design courses usually only transfer knowledge via lectures, and lack interaction, drills and personalized feedback. However, the development and widespread adoption of generative artificial intelligence via the ChatGPT system presents an opportunity to address these issues. Some CIDI model-based ChatGPT systems have been developed, but how to effectively apply these technologies in nursing education design courses remains a challenging problem for researchers. In order to explore the application mode and effect of generative artificial intelligence via ChatGPT technology in nursing education, this study integrated generative artificial intelligence via the ChatGPT system into the teaching activities of nursing and health education design courses, and used computers as learning tools to guide learners to learn nursing and health knowledge. At the same time, two classes of nursing undergraduates were recruited to conduct a quasi-experiment. One of the classes was the experimental group, which used the generative artificial intelligence via the ChatGPT system for learning; the other class was the control group, which used traditional teaching methods for learning. By analyzing learners’ learning efficiency and learning satisfaction, we obtained results about the application effect of generative artificial intelligence via ChatGPT technology in a nursing education design course. According to the experimental results, the generative artificial intelligence via ChatGPT system effectively improved learners’ critical thinking ability, problem solving, and learning enjoyment. These results indicate that the generative artificial intelligence via ChatGPT system has great potential in nursing education design courses, and can improve the deficiencies of traditional teaching methods.

The increase in popularity of Generative Artificial Intelligence Chatbots, or GACs, has created a potentially fruitful opportunity to enhance teaching English as a Foreign Language (EFL). This study investigated the possibility of using GACs to give EFL students metalinguistic guidance (MG) in linguistics courses. Language competency gaps, a lack of individualized engagement, and low metacognitive abilities are common challenges EFL students face in linguistics courses. Feedback has been suggested as a potential solution to these issues in previous studies; nevertheless, conventional corrective feedback (CF) might not fully satisfy the demands of EFL students. In order to address these obstacles, the current study suggested a metalinguistic guiding (MG)-based GAC approach. Using a quasi-experimental approach with pretest and posttest setups, this study evaluated the learning achievement, reflective performance, perception, and metacognitive awareness of EFL students exposed to either CF-based GAC or MG-based GAC. According to the study's findings, the MG-based GAC group performed better than the CF-based GAC group in terms of learning achievement, reflective performance, and perceptual and metacognitive awareness. The GAC's immediate educational usefulness and potential as a pedagogical tool for shaping cognitive processes are highlighted by its successful application in helping EFL students gain metacognitive awareness. This study contributes significantly to the growing body of knowledge about the use of GAC in educational settings by providing empirical evidence of the effectiveness of GAC in terms of delivering MG to EFL students.

In conventional ancient Chinese poetry learning, students tend to be under-motivated and fail to understand many aspects of poetry. As generative artificial intelligence (GAI) has been applied to education, image-GAI (iGAI) provides great opportunities for students to generate visualized images based on their descriptions of poems, and to situate students in a context similar to what a poem describes. In addition, the progressive prompt is a strategy that can progressively provide students with clues and guidance in technology-enhanced learning environments. Hence, this study proposed a progressive prompts-based image-GAI (PP-iGAI) approach to support students' ancient Chinese poetry learning. To evaluate its effectiveness, the present study employed a quasi-experiment design and recruited 80 fifth-grade elementary school students to engage in one of two conditions: one class was assigned as the experimental group and adopted the PP-iGAI approach, while the other class was assigned as the control group and used the conventional prompt-based iGAI (C-iGAI) approach. The results revealed that the PP-iGAI approach could better promote students' learning achievement, extrinsic motivation, problem-solving awareness, critical thinking, and learning performance. In addition, no significant differences were found in the two groups' cognitive load. Moreover, the results of the interview disclosed the learning perceptions and experiences of both groups. Accordingly, the present study can provide a reference not only for ancient Chinese poetry learning but also for the application of GAI in educational fields for future research.

With the wide application of Generative Artificial Intelligence (GenAI) technology in society, GenAI literacy and critical thinking are considered vital competencies for success in the future. As future teachers, pre-service teachers (PSTs) must possess these key competencies. Previous research has pointed out that although learning and applying GenAI knowledge and technology can improve PSTs' GenAI literacy, the results were not as expected. This study incorporated a KWL (Know, Want, Learned)-based reflection strategy based on the SCQA (Situation, Complication, Question, and Answer) model, and proposed an RSCQA (Reflection, Situation, Complication, Question, and Answer) approach, which aims to help PSTs develop GenAI literacy and critical thinking. A quasi-experiment was designed for this study to verify the validity of the RSCQA approach, which was used by the experimental group, while the Conventional SCQA (CSCQA) approach was used by the control group. Results of the study demonstrated that using the RSCQA approach improved PSTs' GenAI literacy and critical thinking, and they excelled in multimodal demonstration capability. This study also conducted an ENA analysis of the content of PSTs' reflections, and the results demonstrated that reflective strategies help PSTs engage in higher-order cognitive activities. The results of the qualitative interviews further indicated that the reflective strategy enhanced PSTs' learning effectiveness and strengthened their critical thinking. Overall, the RSCQA approach helps PSTs engage in reflective learning and perform better in GenAI literacy and critical thinking. The results of this study provide practical experience and operational examples of PST cultivation.

The emergence of Generative AI technologies, represented by ChatGPT, has triggered extensive discussions among scholars in the education sector. While relevant research continues to grow, there is a lack of comprehensive understanding that systematically measures the effects of Generative AI on student learning outcomes. This study employs meta-analysis to integrate findings from previous experimental and quasi-experimental research to evaluate the impact of Generative AI on student learning outcomes. The analysis of 44 effect sizes from 21 independent studies indicates that Generative AI tools, compared to traditional AI tools or no intervention, moderately enhance student learning outcomes (g = 0.572). These tools significantly improve the cognitive (g = 0.604), behavioral (g = 0.698), and affective (g = 0.478) dimensions of learning outcomes. In addition, the study identifies and examines 6 potential moderating variables: educational level, sample size, subject area, teaching model, intervention duration, and assessment instrument. The results of the moderating effects test reveal that sample size and assessment instrument significantly influence the effectiveness of Generative AI. For sample size, the effect of Generative AI on small samples (g = 1.216) is greater than that on medium (g = 0.476) and large samples (g = 0.547). For assessment instrument, the effect of Generative AI on self-developed tests (g = 0.984) is greater than that on standardized tests (g = 0.557). The meta-analysis result indicated that the use of Generative AI should be supplemented with detailed guidance and flexible strategies. Specific recommendations for future research and practical implementations of Generative AI in education are discussed.

Technostress is a phenomenon in which rapid technological advancement affects teachers' psychological well-being. It is an emerging concern in English language education, which may be exacerbated by the advent of generative artificial intelligence (GenAI) tools such as ChatGPT. This study explores the factors that influence technostress among English language teachers using GenAI tools and strategies that can alleviate it. Based on the analysis of qualitative data from semi-structured interviews with 16 instructors at higher education institutions in Hong Kong, the study identifies the rapid advancement of AI technology, inadequate training and lack of experience as contributors to technostress. It also names mitigating strategies including targeted professional development, online engagement and gradual integration. These techniques can foster Technological Pedagogical Content Knowledge (TPACK) and reduce the challenges of incorporating GenAI into English teaching. The findings align with existing literature on the impact of technostress and the role of TPACK. The practical implications include the need for comprehensive training, supportive communities and a balanced approach to AI integration. This investigation also expands the theoretical understanding of technostress in English language teaching and the use of GenAI tools, providing empirical support for existing frameworks. It also suggests directions for future research, which could investigate teacher well-being, effective AI integration and the impact of TPACK.

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.

Ethical decision-making is challenging for most students. Values clarification exercises (VCEs) can help reduce decisional conflicts and feelings of regret. Scholars have suggested Ethical decision-making is challenging for most students. Values clarification exercises (VCEs) can help reduce decisional conflicts and feelings of regret. Scholars have suggested designing values deliberation exercises based on moral dilemma scenarios to help students to identify their values system. However, such exercises are challenging to complete for most teachers and students. Therefore, the development of artificial intelligence (AI)-supported decision aids is warranted. Studies have revealed that using a one-on-one interactive chatbot is a feasible learning strategy for improving the dialectic skills of students. Thus, this study proposed a human-machine learning framework that helps students to perform values clarification in the context of moral dilemmas. To assess the effectiveness of the framework, the present study incorporated the chatbot Chat Generative Pre-trained Transformer into the business ethics course of a university to develop a generative-AI-chatbot-assisted VCE (GAIC-VCE) system for university students. In total, 70 university students were recruited and divided into an experimental group and a control group. The experimental group completed GAIC-VCEs, whereas the control group completed conventional VCEs. The results revealed that the GAIC-VCE system effectively improved the experimental-group students' ethical self-efficacy and ethical decision-making confidence and reduced their decisional conflicts.

Professional Identity Formation (PIF) is considered a crucial process in medical education. It involves how medical students identify their role as physicians, discover their professional positioning, and gradually develop their professional identity through social interactions. This qualitative descriptive study adopted the phenomenological method; it proposed the DSCOR (Diverse thinking, Seeking advice, Construction, Organizing and sharing, and Reflection) model based on the ChatGPT as a life coach (ChatGPT-LC) self-regulated learning (SRL) approach to design a PIF course for six medical students. Data collection included digital storytelling created using AI-generated techniques, learning sheets, direct observations, reflective feedback forms, and semi-structured interviews. The data transcription and analysis were conducted using Colaizzi's method. The results revealed three benefits of Generative AI (i.e., ChatGPT), namely ＂increasing motivation for planning PIF,＂ ＂strengthening the mastery of PIF,＂ and ＂broadening the perspectives of PIF.＂ Moreover, the ChatGPT-LC SRL approach had a positive influence on students, helping them understand the significance of PIF in their personal development at the early stage. The artificial intelligence-generated content provided positive guidance and supportive learning, offering specific suggestions and assistance. This brought about benefits for learning, and provided initial evidence for the application of ChatGPT-LC in medical education.

This paper presents the integration of generative artificial intelligence (GAI) and other AI tools in English as a Foreign Language (EFL) education, addressing the limitations of traditional pedagogical approaches. Conventional EFL methods, often reliant on rote learning for standardized tests, struggle to impart practical language skills relevant to real-world scenarios. By leveraging AI technologies, this study proposes innovative solutions to these challenges, creating authentic, context-rich learning environments and facilitating creative integration of technology for language acquisition. The synergy between GAI and other technology tools enables the creation of immersive language scenarios, offering tailored exercises and narratives that cater to individual proficiency levels and learning objectives. This collaborative approach empowers both teachers and learners to generate their own content, thereby enhancing comprehension and confidence across diverse linguistic contexts. By transcending traditional teaching methods, the integration of GAI with other tools emerges as a transformative catalyst in language education, providing learners with authentic language practice opportunities and empowering them to engage with English in innovative and personalized ways. The paper concludes by urging EFL educators to embrace AI not merely as supplementary resources but as integral components in redefining pedagogical strategies, ensuring the development of more engaging, tailored, and effective language learning environments.