Online learning has become increasingly popular, making the learning process more attractive. One of the most popular learning media is artificial intelligence (AI). However, students do not accept this technology at all. Therefore, this study examined the factors influencing accounting students' acceptance of AI in learning. The survey was conducted with 147 higher-education students who use AI as a learning medium. The data were analyzed using SmartPLS 4.0 with the partial least square approach. The results showed that perceived usefulness influenced behavioral intention to use and satisfaction. However, perceived ease of use was only significant for satisfaction. Similarly, perceived confidence must be consistent with intention. Although it may influence perceived usefulness, other constructs, such as AI quality and personal innovativeness, can increase students' perceptions of the benefits and convenience of adopting AI in learning. Thus, this study contributes to the development of the technology acceptance model (TAM) and the information systems success model and is helpful to scholars, especially in applying AI in learning. They need to pay attention to the quality of AI, such as the accuracy of the information produced. Thus, the need to control the information from the AI only serves as a reference without requiring you to trust it completely.

This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher technical skills, pedagogical content knowledge and teacher satisfaction in using Artificial Intelligence (AI)-Powered Facilitator for designing lectures. The study used a non-random sample technique, and 208 participants answered a survey via Google Form after one semester, using a 5-point Likert scale to rate their responses. The structural equation model was used to analyze the data, and six factors were included in the study. The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction. However, the study also revealed that teacher technical skills have a negative effect on teacher satisfaction, and pedagogical content knowledge has no significant effect. The proposed conceptual model explained 55.7% of the variance in teacher satisfaction Theoretical and practical implications were also discussed. These findings provide insights into the factors that contribute to teacher satisfaction in utilizing AI-Powered Facilitator for designing lectures and could inform the development of effective teacher training programs.

Teachers' learning styles are a crucial part of the learning process as they determine how teachers' brains capture and integrate information linked with the senses. Kurnia, identified as an auditory teacher, was expected to capture written information in a provided numeracy problem. Nevertheless, she prefers to capture visual information, like tables or figures, and utilize them to develop thought-provoking questions. Thus, this study intends to investigate her reasons and the factors affecting Kurnia's decision to utilize visual information as a reference in developing questions. This research adopts a qualitative design covering a case study. Kurnia was selected from 32 teachers from 28 schools; roughly 43% were from public schools, and 57% from private schools. Kurnia placed more emphasis on pictorial information before proposing questions, which was caused by situational factors: the subject matter, the grade level, the student's engagement in the class, the teacher's experience, the teaching experience, and the diversity of students' learning styles. This article recommends that teachers recognize their learning styles to know their strengths and weaknesses in teaching mathematics, and that they convey understandable information utilizing effective instructional methods that represent each learning style of students in the classroom.

Higher education institutions have focused their efforts on promoting research seedbeds as a strategy for formative research. In this regard, the impact of such a strategy remains unknown due to the lack of models that enable its evaluation. Therefore, this study aimed to design an evaluation model for the academic productivity of research seedbeds based on the available evidence in the literature. To achieve this, a systematic review was conducted following the PRISMA model, analyzing 53 documents including articles, book chapters, and conference proceedings from the SCOPUS, ProQuest, Jstor, Scielo, and ScienceDirect databases. The results identified indicators that allowed for the design of a model based on six constructs: research training, institutional capabilities, bibliographic production, innovation and development, social appropriation of knowledge, and human resource training. It was concluded that the indicators evaluating research seedbeds seek greater scientific development involving students and improving the quality of research products, which directly impacts the institutional research mission.

The world eagerly anticipates advancements in AI technologies, with substantial ongoing research on the potential AI applications in the domain of education. The study aims to analyse publications about the possibilities of artificial intelligence (AI) within higher education, emphasising their bibliometric properties. The data was collected from the Scopus database, uncovering 775 publications on the subject of study from 2000 to 2022, using various keywords. Upon analysis, it was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022. China and the United States emerged as the most influential countries regarding publications in this area. The findings revealed that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals. “S. Slade” and “P. Prinsloo” received the most citations, making them highly effective researchers. The co-authorship network primarily comprised the United States, Saudi Arabia, the United Kingdom, and China. The emerging themes included machine learning, convolutional neural networks, curriculum, and higher education systems are co-occurred with AI. The continuous expansion of potential AI technologies in higher education calls for increased global collaboration based on shared democratic principles, reaping mutual advantages.

eTwinning is a community of European schools that promotes networking and transnational collaboration projects. Therefore, as part of the Initial Teacher Education (ITE) initiative, we decided to explore how the research projects on scientific topics familiarize pre-service primary school teachers with eTwinning. 251 pre-service primary school teachers from Spain, Iceland, and Slovenia designed and carried out joint research projects on scientific topics. The aim was to gain insight into their self-assessment of their knowledge of the platform, the limitations of working with it, and their responses. An electronic questionnaire was used as the main instrument for data collection. It turned out that the pre-service teachers enjoyed this international collaboration, but also found it challenging. By the end of the projects, they were familiar with the eTwinning platform, although the least engaged prospective teachers recognized that they needed strong support and considered the platform a non-intuitive environment. Overall, they were willing to use the platform with pupils in the future, which is in line with the aim of the ITE initiative.

This comprehensive systematic review delves into the increasing prevalence of integrating chatbots into language education. The general objective is to assess the current landscape of knowledge regarding chatbot utilisation and its influence on three crucial elements: students' skills, attitudes, and emotions. Additionally, the review seeks to scrutinise the advantages linked to incorporating chatbots in foreign language teaching, exploring their potential benefits while considering limitations and potential negative impacts on specific skills or user experiences. Consequently, this research offers valuable insights into the application of chatbots in foreign language education, shedding light on their potential advantages and areas that warrant further exploration and enhancement. The integration of chatbots in language learning, despite certain limitations, generally yields positive outcomes and enhances educational results in students' skills. Its characteristics can also influence a language learner's attitude, impacting factors such as motivation, interest, autonomy in learning, and engagement or even their sense of fun. Additionally, chatbots prove to be helpful in creating emotionally positive learning environments and can contribute to boosting students' self-esteem and self-confidence.

In an era where diversity and digitalization significantly influence higher education, understanding and adapting to various learning preferences is crucial. This study comprehensively analyzes 394 scholarly articles from 1984 to 2022 using bibliometric methods, providing a dynamic overview of the research patterns in learning styles within higher education. We identified four stages of development during this period: 1984–1995 (Low-interest), 1996–2005 (Early development), 2006–2018 (Development), and 2019–2022 (Intensification). Our analysis highlights that the United States, the United Kingdom, and Australia were the top three leading publishers of research on learning styles in higher education. The results reveal three main topics of publications: educational technology, learning environments, and subject behaviors. This research not only identifies emerging research topics but also underscores the importance of adapting instructional strategies to diverse learning styles to enhance educational outcomes in higher education.

The world is looking forward to advancements in artificial intelligence (AI) technology, with significant research underway regarding the application of AI in education. This study analyzed publications on the potential of AI in secondary schools, focusing on its bibliometric aspects. Data from the Scopus database revealed 1,764 publications from 2019 to 2024. The analysis showed a steady annual growth in publications in this area. China and the USA were the leaders in the number of publications. Xiaoyue Wang was the most prolific researcher, having authored 71 AI-related articles. Yueying Li, Xiaoxu Chen, Yanzhu Zhang, and Yi Liu contributed to the field with 56, 55, 53, and 51 articles, respectively. The themes that emerged from 2019 to 2022 are related to media, application, study, institutions, artificial, digital, learning, factors, development, technologies, medical, automated, perception, support, and sustainability. From 2023 to 2024, the topics discussed in AI are related to students, education, perception, algorithms, digital, prediction, networks, challenges, writing, teachers, AI-powered, curriculum, century, integration, technology, and framework. The difference in research in 2019-2022 and 2023-2024 is focusing the theme's focus from the general to the specific. The co-occurrence analysis revealed that prominent keywords appeared in 3 clusters. Cluster 1 is the most popular in recent times. It deals with the application, assessment, and management of AI. Cluster 2 relates to AI relationships and models, while Cluster 3 relates to AI data sources.