Search

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.

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.

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.

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.

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 reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010–2014, AIoL research emphasized learner-oriented learning. In 2015–2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. “Intelligent tutoring systems” have played the most important role in AIoL, followed by “profiling and prediction,” and “adaptive systems with personalization.” Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers.

With the rapid development of artificial intelligence, the application of AI robots (Artificial Intelligence-based robots) for instruction has become an attractive research topic. Numerous studies have shown that AI robots may provide new opportunities for learning designs in school settings or professional training. However, there is no review examining the role and research foci of AI-Robots in Education (AIRE) research. This study therefore explored the research trends of AIRE by conducting a systematic review of SSCI (Social Sciences Citation Index) journal articles published in the Web of Science (WoS). The study analyzed the participants, duration of the studies, learning environments, application domains, data analysis, evaluations of learners' performance, learning strategies, roles of AI-robots, and research issues. The research findings are concluded as follows: (1) The countries of Canada, Chile, and South Korea invested in AIRE research early, and focused on students' learning performance and learning behavior. (2) Most AIRE research focuses on research targets under the age of 13 and completed experiments within 4 weeks in a physical environment. Most AIRE research has been applied in the disciplines of Language and Science, and problem-solving related strategies and mixed strategies are the most commonly used strategies. (3) AI-robots are often applied and regarded as tutees or tutors. Regardless of what the AI-robot's role is, learning performance is the most widely focused variable in AIRE research. Furthermore, attitudes, opinions of learners or learning perceptions and learning behavior are other frequently discussed themes. To sum up, this study makes several recommendations for AIRE research for educators, researchers, and policy makers in higher education settings as a reference based on the results.

Writing is a challenging task for students in language learning. A key question in this challenge is how to exploit the potential of information technology and design effective learning strategies to promote quality writing. This is a question that Chinese teachers need to actively consider in the process of optimizing their writing instruction. Therefore, this study embedded problem-based scaffolds in an SVVR system and proposed a second-order progressive problem-based scaffold (SPPS-SVVR) strategy to enhance students' writing skills. To examine the effectiveness of the proposed strategy, a quasi-experiment was conducted in an elementary school in China. The experimental group (39 students) used the SPPS-SVVR writing strategy, while the control group (39 students) used the traditional SVVR writing strategy (T-SVVR). The study results showed that the SPPS-SVVR writing strategy not only significantly improved the students' writing performance, but also increased their learning engagement and transferability. In addition, the results of epistemic network structure analysis showed that students using the SPPS-SVVR writing strategy focused on writing in terms of internal, mental, and dynamic environments. Interviews revealed that the SPPS-SVVR writing strategy supported the experimental group students' grasp of writing ideas and verbal expressions, and they were more satisfied with this strategy.

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.

This study conducted a systematic review to identify the mainstreams of contemporary digital reading research. We performed a co-citation network analysis to examine the streams and features of key topics in digital reading research. We also performed content analysis and used the bibliometrix R-package to map digital reading research themes and identify several trends in the field. In this study, we (a) identified three mainstreams of research within the digital reading co-citation network (namely, differences in reading comprehension between print books and e-books, adolescents' habits and preferences related to digital reading and their digital reading literacy, and the analysis of reading behaviors as well as the use of interactive learning books); (b) determined that most studies on digital reading have employed quantitative or mixed methods, involved the use of tests and questionnaires to evaluate learners' performance, and have not focused on specific application domains or learning strategies; and (c) discovered that most studies on digital reading had investigated the affective aspects of learners' experiences, followed by cognition, learning behavior, and correlation or cause-and-effect analysis. We identified "engagement," "digital media," and "digital literacy" as key topics in research on digital reading. Directions for future research and digital reading education are provided..

In recent years, smart healthcare technologies used to facilitate precision medicine in clinical practice have increasingly become a topic of discussion. Nurses and nurse preceptors are critical professionals in hospitals, and are also the frontline staff using smart healthcare. The purpose of this study was to investigate nurses' and nurse preceptors' perceptions of smart healthcare (including learning motivation, learning attitudes, and personal innovativeness) and the differences in their conceptions of learning in smart healthcare technology contexts. The research findings showed that nurse preceptors had higher mean scores than nurses for learning motivation, learning attitudes, and personal innovativeness in smart healthcare, especially for learning attitudes and personal innovativeness. The nurse preceptors' conceptions of learning in smart healthcare technology contexts were also more diverse than those of nurses. The main differences are that nurses' drawings showed a healthcare setting where learners (both patients and nurses) use computers to connect to cloud services for online visits, and where patients can engage in online health education learning activities via computers or mobile devices. The nurse preceptors' drawings focused on the delivery of nursing knowledge and skills to learners via VR technologies, helping diagnose patients, analyzing medical data, and predicting healthcare through smart healthcare technologies.

In Chinese, writing is a basic competency that pupils should possess. But it is still challenging for teachers to improve pupils' writing abilities. Therefore, this study proposes an intelligence-based cognitive diagnostic feedback strategy to improve pupils' writing ability and writing learning quality by analyzing their writing performance, epistemic network structure, and learning engagement. Thepupils are randomly divided into an experimental group (N = 28) and a control group (N = 28). The experimental group adopts the ICDF strategy, while the control group adopts the WS-TF strategy. The results showed that the I-CDF strategy improved the pupil's writing scores and learning engagement. In addition, the results of the epistemic network analysis showed that the pupils using the ICDF strategy focused on dynamic descriptions such as action, psychology, and environmental changes in vocabulary use, and the epistemic network structure was more complex than the control group. The interviews showed that the pupils in the experimental group were highly satisfied with I-CDF strategy.

Reading comprehension is critical and challenging for the English learning process. EFL (English as Foreign Language) learners often do not understand the meaning of the text due to the lack of an authentic learning context. This easily leads learners to read literally without deeply understanding. To address this issue, an article-structure strategy-based SVVR (short for ASS-SVVR) approach was proposed in the study, in which spherical video-based virtual reality (SVVR) was employed to immerse EFL learners in a real environment, and the article-structure strategy was introduced as step-by-step reading scaffolding to improve reading efficiency, accuracy, and comprehension. To verify the effectiveness of the proposed approach, a quasi-experiment was conducted in an English reading class at a university with two classes of 63 EFL learners. Among them, 33 learners were randomly distributed to the experimental group, conducting the ASS-SVVR approach, while the remaining 30 were assigned to the control group, conducting the conventional SVVR (C-SVVR) approach. The research findings showed that EFL students conducting the ASS-SVVR approach had positive performance in reading comprehension, extrinsic learning motivation, and metacognitive awareness of reading strategy. Moreover, cognitive load and sense of presence were not shown to have a significant difference between the two groups.

This research reviews 56 robots in science education (RSE) research articles published in SSCI (Social Sciences Citation Index) journals in the Web of Science (WoS) database. The analysis results showed that there were three clusters of RSE studies: robots in elementary science education, robot-based STEM activities, and robot-facilitated computational thinking. In terms of the research issues, more studies were conducted across-disciplines. Regarding thematic classifications, "general benefits of educational robots" was the most frequently investigated issue, while "diversity and broadening participation," and "creativity and motivation" were less frequently discussed. The most adopted research methods were quantitative and mixed methods, focusing on measuring learners' cognition and affect. Most of the interventions in the empirical studies adopted pedagogical interventions. In terms of the interaction issue, LEGO was adopted the most, followed by self-made robots; moreover, robots frequently played the role of tools in science education. From the perspective of specific robotics content knowledge, "control" (e.g. programming) comprised the majority. The most adopted learning strategy was project-based learning, followed by issue-based discussion. Regarding assessment issues, the most adopted evaluation approaches to evaluating students' performance were questionnaires, observation, verbal interviews, and artifacts to assess the quality of learning activities.

Second language (L2) writing plays an important role in improving the learners' language skills of English as a Foreign Language (EFL) in terms of language expression and linguistic thinking. Therefore, improving writing skills is still a focus area for EFL learners. To enhance EFL learners' writing ability and optimize their writing quality, an intelligent-based cognitive diagnostic feedback (I-CDF) strategy is proposed based on the Intelligent Writing Critique System (IWCS). IWCS can provide feedback on students' English writing learning components, including lexical, syntactic, rhetorical expression, chapter structure, and discourse intention. Hence, the study intends to assess the effects of feedback strategies based on students' writing scores, self-efficacy, epistemic network structure, and transferability. A quasi-experiment was conducted in two classes at a university in southeastern China, where students were randomly divided into two classes. One (N = 32) was considered an experimental group conducted using the proposed I-CDF strategy, while the other (N = 30) was the control group using the score-based teacher corrective feedback (S-TCF) strategy. The writing experiment lasted for seven weeks. The students would be interviewed at the end of the writing learning activities. The results indicated that the I-CDF strategy improved students' writing scores and self-efficacy. Furthermore, the epistemic network analysis showed that, compared to the control group, the I-CDF strategy encouraged the students to devote more energy to focus on high-level applications of writing skills such as rhetorical expression and sentence structure collation, optimizing the students' writing epistemic network of the writing of the students. The interview revealed that the I-CDF strategy supported the experimental group students' accurate understanding of writing, strengthening the logical structure of the writing. At the same time, the students in the experimental group were satisfied with the I-CDF strategy.

Robotics education has received widespread attention in K-12 education. Studies have pointed out that in robotics courses, learners face challenges in learning abstract content, such as constructing a robot with a good structure and writing programs to drive a robot to complete specific learning tasks. The present study proposed the embodied learning-based computer programming approach and applied it to the LEGO Mindstorms EV3 robotics course. To evaluate its effectiveness, a quasi-experiment was conducted in one public primary school to explore its effects on students' learning achievement, learning motivation, learning attitudes, learning engagement, and cognitive load. The experimental group (40 students) adopted the embodied learning-based computer programming approach, while the control group (40 students) adopted the conventional computer programming approach. The results showed that the experimental group had significantly better learning achievement in robotics than the control group, and that there was no significant difference in the cognitive load of the two groups. In terms of learning motivation, although both groups showed improvement, the experimental group had higher intrinsic learning motivation. In addition, the experimental group outperformed the control group with regard to learning attitudes and learning engagement (including cognitive, behavioral, and emotional engagement). Accordingly, this study could contribute to future research for developing more effective robotics teaching approaches and computer programming activity design.

This study provides research-based evidence to profile: (1) the roles of artificial intelligence in nursing; (2) its research applications; and (3) the research trends for future study. On the basis of the PRISMA statement, a series of AI and nursing education related keywords from the literature were used to retrieve high-quality journal articles from the Web of Science. A total of 112 AI-supported nursing education research articles were analyzed based on a three-dimensional framework, including interaction (e.g. the roles of AI, types of AI systems), research (e.g. methods and fields), and performance (e.g. research groups and measurement foci). The results revealed that AI played a primary role in profiling and prediction in nursing research (63%), and the most used AI system in nursing was intelligent agents (53%). The quantitative approach (87%) was the dominant research method, and the most relevant studies concerned health and medicine (92%). Regarding sample and measurement matters, patients and medical staff (75%) were the two primary research samples, and the performance evaluation of AI-related tools and systems (90%) was the core measurement focus. Additional content analysis across the three research interests was performed and discussed. Directions for future studies are provided.

This research reviewed the publications in the Scopus database in the museum-based mobile learning field based on the technology-based learning review model from 2008 to 2019. The aim of this study was to investigate trends in the field of museum-based mobile learning from the main journals and papers, including countries and areas, participants, research methods, learning domains, the orientation of adopted sensing or location-based technologies, learning strategies, and research topics. The cluster and co-occurrence analysis methods were used to analyze the main key terms and the relationships between them. Three thematic clusters, namely strategies and media for mobile-based museum learning, personalized supports for mobile-based museum learning, and applications of mobile-based museum learning were identified. The results show that inquiry-based learning was the most adopted learning strategy for higher education, while contextual mobile learning was the most frequently employed strategy for elementary school students; such extensively adopted mobile learning strategies as peer assessment, project-based learning, video sharing, and synchronous sharing were not adopted in the studies. In addition, an important finding in the literature of the main research purpose of museum-based mobile learning research is the affective domain, followed by the influence of learner characteristics and the effects of mobile learning.

This study investigates the roles, applications, and trends of concept map-supported 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 co-citation relationships, are taken into account based on the technology-based 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.

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 AI-supported 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 RTP-AIEW 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.

Information literacy is a core research topic in the field of library and information science. The developmental context of this field can be examined through a long-term retrospective analysis of relevant literature. This study explored the research trends and potential research issues in the top 100 most frequently cited articles on information literacy in higher education published from 2011 to 2020. In addition to a systematic review, this study employed bibliometric methods, including co-citation network analysis, to identify four main research streams in the field of information literacy in higher education: (a) the relationships among students' information literacy beliefs, competencies, attitudes and behaviour; (b) teachers', librarians' and students' perspectives on information literacy; (c) the relationship between students' information literacy and epistemic beliefs; and (d) the web search behaviour of digital natives. Accordingly, potential directions for future research and practitioner notes related to information literacy in higher education are proposed herein as a reference for researchers, teachers and policymakers. Implications for practice or policy:. • For administrators of higher educational institutions, understanding the challenges of new digital technologies and providing training to develop information literacy skills are crucial. • For teachers, designing teaching materials and pedagogy based on the latest information literacy standards and framework is useful. • Collaboration with professionals from different disciplines is also useful for teachers to integrate information literacy into subject learning activities to cultivate the information literacy competencies of students. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) in higher education has proven to be a useful learning technology; it can help learners achieve positive learning outcomes in the learning environment and can also enable teachers to better understand learners' learning status and further improve their teaching strategies. This study reviewed the top 50 AI in higher education studies in the Web of Science database from the perspective of highly cited papers and based on a technologybased learning model. The results show that predictions of learners' learning status (including dropout and retention, student models, and academic achievement) are most frequently discussed in the AI in higher education studies; AI technology is most commonly applied in engineering (including computer courses); AI technologies most often play the role of profiling and prediction in higher education, followed by intelligent tutoring systems and assessment and evaluation. In terms of research issues, the most frequently discussed issues are learning behaviour, accuracy, sensitivity and precision, cognition and affect. Learners' higher order thinking skills, collaboration or communication, self-efficacy or confidence and skills are less frequently discussed in AI in higher education studies. Accordingly, we propose potential research issues and practitioner notes for AI in higher education as a reference for researchers, educators and decision-makers. [ABSTRACT FROM AUTHOR]