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Abstract This preliminary study explores how GPT-4 Vision (GPT-4V) technology can be integrated into teacher analytics through observational assessment, aiming to improve reflective teaching practice. Our study develops a Video-based Automatic Assessment System (VidAAS) powered by GPT-4V. This approach uses Generative Artificial Intelligence (GenAI) to provide detailed insights into classroom dynamics. Our study encompasses various methods with multiple steps: a comprehensive literature review, prototype development of the VidAAS, and usability testing with in-service teachers. The study findings reveal that VidAAS demonstrates high accuracy in evaluating skills in the behavioral (psychomotor) domain and offers comprehensive explanations for each assessment. While showing promise in these areas, the system also indicates potential for further enhancement in processing speed and refinement in assessing cognitive and affective domains. We discuss how VidAAS supports teachers’ reflection-in-action and reflection-on-action, emphasizing the need to balance AI-driven insights and human judgment. Our study findings also guide future research avenues for VidAAS design, implementation, and integration in teacher analytics, underscoring GPT-4V’s potential for real-time, scalable feedback and a deeper classroom understanding.

Debate is a universally acknowledged competency for its vital role in fostering essential skills such as analytical reasoning, eloquent communication, and persuasive argument construction. This is relevant in both formal educational settings like classrooms and informal venues such as after-school clubs. Traditional debate training methods often face challenges in facilitating personalized learning, primarily due to the difficulty of securing a debate partner. However, the emerging field of generative AI offers a promising alternative. This study evaluates the effectiveness of DEBO, a debate education application that utilizes ChatGPT, an advanced language model by OpenAI. Focusing on university students actively involved in a debate club, six volunteers tested DEBO and participated in interviews to provide insights. The findings reveal that DEBO notably enhances divergent thinking and features a comprehensive analytics dashboard for in-depth performance analysis. However, there are few limitations including occasional shortcomings in debate quality, minor inaccuracies, and the need for faster response times. The study underscores the transformative potential of advanced language models like ChatGPT in reshaping debate education, while also identifying areas for further improvement.

Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and challenging endeavor for most students, can be more accessible with a generative AI enabled conversation partner that provides tailored questions and encourages the audience to deeply appreciate artwork. This study explores the application of multimodal large language models (MLLMs) in art appreciation education, with a focus on developing LLaVA-Docent, a model designed to serve as a personal tutor for art appreciation. Our approach involved design and development research, focusing on iterative enhancement to design and develop the application to produce a functional MLLM-enabled chatbot along with a data design framework for art appreciation education. To that end, we established a virtual dialogue dataset that was generated by GPT-4, which was instrumental in training our MLLM, LLaVA-Docent. The performance of LLaVA-Docent was evaluated by benchmarking it against alternative settings and revealed its distinct strengths and weaknesses. Our findings highlight the efficacy of the MMLM-based personalized art appreciation chatbot and demonstrate its applicability for a novel approach in which art appreciation is taught and experienced.

Chronic respiratory disease is among the most common non-communicable diseases, and particulate materials (PM) are a major risk factor. Meanwhile, evidence of the relationship between the physicochemical characteristics of PM and pulmonary toxicity mechanism is still limited. Here, we collected particles (CPM) from the air of a port city adjacent to a cement factory, and we found that the CPM contained various elements, including heavy metals (such as arsenic, thallium, barium, and zirconium) which are predicted to have originated from a cement plant adjacent to the sampling site. We also delivered the CPM intratracheally to mice for 13 weeks to investigate the pulmonary toxicity of inhaled CPM. CPM-induced chronic inflammatory lesions with an increased total number of cells in the lung of mice. Meanwhile, among inflammatory mediators measured in this study, levels of IL-1β, TNF-α, CXCL-1, and IFN-γ were elevated in the treated group compared with the controls. Considering that the alveolar macrophage (known as dust cell) is a professional phagocyte that is responsible for the clearance of PM from the respiratory surfaces, we also investigated cellular responses following exposure to CPM in MH-S cells, a mouse alveolar macrophage cell line. CPM inhibited cell proliferation and formed autophagosome-like vacuoles. Intracellular calcium accumulation and oxidative stress, and altered expression of pyrimidine metabolism- and olfactory transduction-related genes were observed in CPM-treated cells. More interestingly, type I-LC3B and full-length PARP proteins were not replenished in CPM-treated cells, and cell cycle changes, apoptotic and necrotic cell death, and caspase-3 cleavage were not significantly detected in cells exposed to CPM. Taken together, we conclude that dysfunction of alveolar macrophages may contribute to CPM-induced pulmonary inflammation. In addition, given the possible transformation of heart tissue observed in CPM-treated mice, we suggest that further study is needed to clarify the systemic pathological changes and the molecular mechanisms following chronic exposure to CPM.

To date, documentation of two doses of measles-containing vaccine (MCV) has been accepted as confirmation of measles immunity among healthcare workers (HCWs). However, we encountered measles in an HCW who had received two doses of MCV. A patient with measles was admitted to our hospital. Among 62 exposed HCWs, one nurse who had previously received two doses of MCV was shown to be negative for anti-measles immunoglobulin G (IgG), and was confirmed to have measles 14 days after exposure. Based on this experience, we suggest that all HCWs should be tested for anti-measles IgG to confirm their immunity to measles. [ABSTRACT FROM AUTHOR]