The fundamentals of physics laboratory for teachers course at Walailak University, Thailand, has been revamped post-COVID-19 to incorporate problem-based learning (PBL) alongside traditional labs. This approach, aligned with the Thailand qualification framework and the United Kingdom professional standards framework, aims to equip future science educators with essential skills. PBL fosters collaborative learning, focusing on experimental design using various tools. Students work in groups, and their assessment, based on rubrics, includes formative evaluation contributing 20% to the course grade. Based on the improvements of completion over progress scores, the PBL process and iterative testing enhanced experimental design, data analysis, presentation skills, adaptability, and problem-solving abilities of the group members. This course demonstrates the potential applicability of PBL to other science laboratory courses, enhancing educators’ preparedness for diverse teaching contexts.

The Journal of the Korean Wood Science and Technology (JKWST) launched in 1973 as an official publication of the Korean Society of Wood Science and Technology has been served as a core of knowledges on wood science and technology.

This exploratory case study examines the feasibility of integrating generative artificial intelligence (AI) into a low-cost Kundt’s tube activity in pre-service science teacher education. The proposed Do-It-With-AI framework comprises four phases: conceptual exploration, design development, troubleshooting, and extension to real-world applications. Scopus AI was used to support the literature-oriented conceptual exploration, while ChatGPT-4 and ChatGPT-5 supported the generation of design alternatives, troubleshooting hypotheses, and possible extensions. Three pre-service teachers constructed and tested the apparatus, and successive configurations were evaluated through resonance measurements and estimation of the speed of sound. The case illustrates how AI-generated suggestions can be incorporated into an iterative design–test–verification process in which proposed modifications are examined against physical principles and experimental evidence. The four-phase sequence is therefore presented as a feasible framework for integrating generative AI with hands-on experimentation, with instructor-guided verification serving as an essential component. Because the present study focuses on feasibility and process rather than educational outcomes, further investigation with larger cohorts and explicit measures of student reasoning, learning, collaboration, and AI literacy is required. The principal contribution of the Kundt’s tube case is to demonstrate a structured and empirically grounded use of generative AI in low-cost experimental development within pre-service science teacher education.

In this work, we study the non-minimally-coupled Higgs model in the context of warm inflation scenario on both metric and Palatini approaches. We particularly consider a dissipation parameter of the form $Γ=C_{T}T$ with $C_{T}$ being a coupling parameter and focus only on the strong regime of the interaction between inflaton and radiation fluid. We compute all relevant cosmological parameters and constrain the models using the observational Planck 2018 data. We discover that the $n_s$ and $r$ values are consistent with the observational bounds. Having used the observational data, we constrain a relation between $ξ$ and $λ$ for the non-minimally-coupled warm Higgs inflation in both metric and Palatini cases. To produce $n_s$ and $r$ in agreement with observation, we find that their values are two orders of magnitude higher than those of the usual (cold) non-minimally-coupled Higgs inflation.