This study examines the comparative teaching effectiveness and student satisfaction between native Japanese language teachers (NJLTs) and non-native Japanese language teachers (NNJLTs). Utilizing a sample of 740 students from various educational institutions in Japan, the research employs a quantitative design, including structured questionnaires adapted from established scales. Advanced statistical methods, including factor analysis and multiple regression, were used to analyze the data. The findings reveal no significant differences in student satisfaction and language proficiency between students taught by NJLTs and NNJLTs. Additionally, regression analysis showed that cultural relatability and empathy were not significant predictors of teaching effectiveness, suggesting that factors beyond nativeness influence student outcomes. These results challenge the native-speakerism ideology, highlighting the importance of pedagogical skills, teacher-student rapport, and effective teaching strategies. The study underscores the need for inclusive hiring practices, comprehensive teacher training programs, and collaborative teaching models that leverage the strengths of both NJLTs and NNJLTs. Implications for educational policy, curriculum design, and teacher professional development are discussed, advocating for a balanced approach that values the contributions of both native and non-native teachers. Limitations include the reliance on self-reported data and the specific cultural context of Japan. Future research should explore additional variables, employ longitudinal designs, and utilize mixed-methods approaches to provide a more nuanced understanding of language teaching effectiveness.

Language is fundamental to human communication, allowing individuals to express and exchange ideas, thoughts, and emotions. In early childhood, some children experience communication disorders that impede their ability to articulate words correctly, posing significant challenges to their learning and development. This issue is exacerbated in developing countries, where limited resources and a lack of technological tools hinder access to effective speech therapy. Traditional speech therapy remains vital, but the latest technological advancements have introduced robotic assistants to enhance therapy for communication disorders. Despite their potential, these technologies are often inaccessible in developing regions due to high production costs and a lack of sustainable manufacturing models. For these reasons, this paper presents “FONA,” a robotic assistant that employs rule-based expert systems to provide tactile, auditory, and visual stimuli. FONA supports children aged 3 to 6 in speech therapy by delivering exercises such as syllable production, word formation, and pictographic storytelling of various phonemes. Notably, FONA was successfully tested on children with cochlear implants, reducing the number of sessions required to produce isolated phonemes. The paper also introduces an innovative analysis of the Make To Order (MTO) manufacturing system for producing FONA in developing countries. This analysis explores two key perspectives: collaborative networks and entrepreneurship, offering a sustainable production model. In a pilot experiment, FONA significantly improved children’s attention spans, increasing the period by 17 min. Furthermore, the economic analysis demonstrates that producing FONA through collaborative networks can significantly reduce costs, making it more accessible to institutions in developing countries. The findings suggest that the project is viable for a five-year period, providing a sustainable and effective solution for addressing communication disorders in children.

Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

This article delves into the controversial practice of utilizing a student’s first language (L1) as a teaching resource in second language (L2) learning environments. Initially, strategies such as code-switching/code-mixing and translanguaging were considered signs of poor linguistic ability. There was a strong push towards using only the target language in foreign language education, aiming to limit the first language’s interference and foster a deeper immersion in the new language. However, later research has shown the benefits of incorporating the first language in bilingual education and language learning processes. It’s argued that a student’s knowledge in their native language can actually support their comprehension of a second language, suggesting that transferring certain linguistic or conceptual knowledge from L1 to L2 can be advantageous. This perspective encourages the strategic use of this knowledge transfer in teaching methods. Moreover, the text points to positive results from various studies on the positive impact of L1 usage in L2 classrooms. These insights pave the way for further exploration into the application of the first language in adult English as a Second Language (ESL)/English as a Foreign Language (EFL) education, particularly regarding providing corrective feedback.